ÍNDICE

1 LOAD DATA

The database object of analysis was provided by the Spanish Institute of Oceanography whic contain fishing data about 49 Spanish Mediterranean harbours of six species of small pelagic fish: bogue (Boops boops), sardine (Sardina pilchardus), anchovy (Engraulis encrasicoulus), sardinella (Sardinella aurita), jack mackerels (Trachurus sp.) and mackerel (Scomber sp.).

The database have information from 2009 to 2021, but not every year there are catches of the objective species in all horbours. The harbours are divided in two fishing areas: Alboran Sea GSA-01 and Northern Spain GSA-06, in this workflow both areas are analyzed separately

The data from the year 2020 and 2021 are excluded from this analysis due to the unusual fisheries activity as a result of the quarantine period of COVID-19. In this step 5 harbours are excluded of the analysis.

The variables are numeric, except harbours and fishing areas. The row names are defined as the variable composed by the name of the harbours and the year (harbour_year).

On the other hand, a dataset is obtained in which the number of variables is reduced. In this step, the variable species is created in which the species is indicated and the variables of the different species are merged in captures, fishing days and boats. This dataset is created to improve the visualization of data in Exploratory Data Analysis. As in the previous dataset, data from years 2020 & 2021 are excluded.

Finally, a dataset is created for each species. NA data are excluded from these datasets.

2 EXPLORATORY DATA ANALYSIS

The dataset has a dimension of n=399 x p=22 variables, with fishing data from 44 harbours and 18 variables (3 per species). The structure of the data for the sardine and anchovy species in the fishing area PS-SPF-G1 for the year 2009 is show below as an example of the dataset.
Table 1. Example of dataset contain. Fishing area Alboran Sea GSA-01 in 2009. Species: Sardine and anchovy
fisherie_area year harbour anchovy_catches anchovy_fishingdays anchovy_boats sardina_catches sardina_fishingdays sardina_boats
PS-SPF-G1 2009 Adra NA NA NA 239612.14 222 13
PS-SPF-G1 2009 Águilas NA NA NA 854.90 12 6
PS-SPF-G1 2009 Algeciras 5625.0 45 9 36210.00 139 14
PS-SPF-G1 2009 Almería 654.0 28 11 624306.00 689 31
PS-SPF-G1 2009 Carboneras NA NA NA 8947.00 8 4
PS-SPF-G1 2009 Cartagena NA NA NA 2748.00 4 2
PS-SPF-G1 2009 Estepona 9495.6 7 4 1033125.00 507 17
PS-SPF-G1 2009 Fuengirola 48935.0 86 10 423438.30 367 18
PS-SPF-G1 2009 Garrucha NA NA NA 3011.95 4 3
PS-SPF-G1 2009 La Atunara NA NA NA 1452.00 10 2
PS-SPF-G1 2009 Málaga 49532.0 240 27 622356.00 780 31
PS-SPF-G1 2009 Marbella 28.0 1 1 739592.00 391 17
PS-SPF-G1 2009 Mazarrón 3830.0 9 7 24570.00 70 16
PS-SPF-G1 2009 Motril 1020.0 4 2 343989.00 308 12
PS-SPF-G1 2009 Vélez-Málaga 175340.5 367 24 1867912.50 1218 30

The dataset for fish zone GSA-01 has a dimension of n=169 x p=22 with fishing data from 16 harbours. The dataset for fish zone GSA-06 has a dimension of n=230 x p=22 with fishing data from 28 harbours.

2.1 DESCRIPTION

The function describe() from ‘dlookr’ offer the description of the variables per species and year and the function ggplot() from ‘ggplot2’ is used to visualize the relation between variables.
Table 2. Alboran Sea GSA-01: Variables description per species and year
described_variables species year n na mean sd se_mean IQR skewness kurtosis
boats anchovy 2009 9 6 1.055556e+01 9.180293e+00 3.060098e+00 7.000 1.0485351 0.0107928
boats anchovy 2010 12 4 1.258333e+01 1.146107e+01 3.308525e+00 14.250 1.0136066 0.0082213
boats anchovy 2011 11 5 1.281818e+01 9.785890e+00 2.950557e+00 15.000 0.6093832 -1.2099160
boats anchovy 2012 12 4 1.300000e+01 9.936160e+00 2.868322e+00 16.500 0.5658222 -1.1962154
boats anchovy 2013 13 3 1.438462e+01 1.280975e+01 3.552786e+00 24.000 0.4568388 -1.7824793
boats anchovy 2014 12 3 1.466667e+01 1.322761e+01 3.818482e+00 23.500 0.4610335 -1.5673914
boats anchovy 2015 13 2 1.538462e+01 1.434421e+01 3.978369e+00 21.000 0.8532820 -0.9370290
boats anchovy 2016 14 1 1.500000e+01 1.359298e+01 3.632878e+00 13.750 1.5751440 2.2381633
boats anchovy 2017 15 0 1.053333e+01 9.387428e+00 2.423824e+00 7.000 1.4961836 1.6619803
boats anchovy 2018 15 0 1.446667e+01 1.338905e+01 3.457038e+00 15.000 1.4741788 1.3547217
boats anchovy 2019 14 1 2.085714e+01 1.496075e+01 3.998430e+00 24.500 0.4919785 -1.3759585
boats bogue 2009 13 2 4.307692e+00 4.697244e+00 1.302781e+00 4.000 2.3197797 6.2423044
boats bogue 2010 14 2 5.071429e+00 5.075799e+00 1.356564e+00 4.500 2.1749194 5.6317545
boats bogue 2011 11 5 6.727273e+00 5.312079e+00 1.601652e+00 5.000 1.6291973 3.4471283
boats bogue 2012 12 4 6.250000e+00 5.361903e+00 1.547848e+00 4.250 1.6684243 3.2828019
boats bogue 2013 13 3 4.153846e+00 4.469268e+00 1.239552e+00 2.000 2.7992154 8.9036953
boats bogue 2014 13 2 4.769231e+00 6.166493e+00 1.710278e+00 5.000 2.8808426 9.2072006
boats bogue 2015 15 0 4.733333e+00 5.020909e+00 1.296393e+00 4.000 2.6700759 8.4822811
boats bogue 2016 12 3 4.833333e+00 4.932883e+00 1.424001e+00 5.000 1.5812760 2.3321995
boats bogue 2017 10 5 4.300000e+00 6.000926e+00 1.897659e+00 1.750 2.9108436 8.8177275
boats bogue 2018 10 5 3.000000e+00 3.055051e+00 9.660918e-01 1.500 2.3965000 6.0934767
boats bogue 2019 14 1 3.642857e+00 4.180672e+00 1.117332e+00 2.500 2.8654640 9.0489325
boats sardine 2009 15 0 1.440000e+01 1.016858e+01 2.625516e+00 12.500 0.4704671 -0.7654765
boats sardine 2010 15 1 1.606667e+01 1.351437e+01 3.489394e+00 23.500 0.5611605 -1.1496343
boats sardine 2011 16 0 1.325000e+01 1.140468e+01 2.851169e+00 21.000 0.4853047 -1.3745704
boats sardine 2012 16 0 1.331250e+01 1.071895e+01 2.679737e+00 15.500 0.3899214 -1.1594547
boats sardine 2013 15 1 1.460000e+01 1.233346e+01 3.184486e+00 21.500 0.4449479 -1.5059960
boats sardine 2014 14 1 1.628571e+01 1.302913e+01 3.482182e+00 25.750 0.4173564 -1.6261621
boats sardine 2015 13 2 1.815385e+01 1.589993e+01 4.409846e+00 17.000 0.7341431 -0.9132564
boats sardine 2016 13 2 1.761538e+01 1.558064e+01 4.321293e+00 27.000 0.7252976 -1.0213486
boats sardine 2017 14 1 1.014286e+01 9.197467e+00 2.458127e+00 7.750 1.3819081 1.3409755
boats sardine 2018 15 0 1.420000e+01 1.180315e+01 3.047560e+00 16.000 0.8908964 -0.3155491
boats sardine 2019 15 0 1.306667e+01 1.178659e+01 3.043286e+00 17.500 0.8976434 -0.8857029
boats sardinella 2009 12 3 9.833333e+00 7.145670e+00 2.062777e+00 7.500 1.2548363 0.9936409
boats sardinella 2010 13 3 6.846154e+00 5.683986e+00 1.576454e+00 5.000 1.7070441 2.5432165
boats sardinella 2011 11 5 8.818182e+00 9.163167e+00 2.762799e+00 7.000 1.7757399 2.7356902
boats sardinella 2012 13 3 8.076923e+00 8.995013e+00 2.494768e+00 7.000 1.7747214 2.4960187
boats sardinella 2013 14 2 6.857143e+00 7.833431e+00 2.093572e+00 6.250 1.6037492 1.6357621
boats sardinella 2014 14 1 1.157143e+01 9.756373e+00 2.607500e+00 15.750 0.5220347 -1.3420589
boats sardinella 2015 15 0 8.533333e+00 7.288608e+00 1.881911e+00 9.500 0.8641427 -0.6363614
boats sardinella 2016 14 1 7.571429e+00 8.482691e+00 2.267094e+00 6.750 1.7896050 2.8266255
boats sardinella 2017 13 2 7.384615e+00 8.160254e+00 2.263247e+00 7.000 1.5746739 1.7326912
boats sardinella 2018 15 0 6.600000e+00 6.231258e+00 1.608904e+00 6.000 1.8596368 3.7107216
boats sardinella 2019 15 0 7.866667e+00 7.039345e+00 1.817551e+00 5.500 1.6557241 2.1163073
boats scomber 2009 15 0 1.153333e+01 7.576907e+00 1.956349e+00 5.000 1.4626730 1.6515760
boats scomber 2010 14 2 1.107143e+01 8.128433e+00 2.172415e+00 6.000 1.2116498 0.7441378
boats scomber 2011 14 2 1.171429e+01 8.765567e+00 2.342696e+00 9.000 1.1497361 0.7292059
boats scomber 2012 16 0 1.143750e+01 9.932900e+00 2.483225e+00 8.250 1.3174080 0.9692951
boats scomber 2013 14 2 1.121429e+01 8.868081e+00 2.370094e+00 12.250 0.9287724 -0.6163391
boats scomber 2014 14 1 1.142857e+01 9.296106e+00 2.484489e+00 12.250 0.8486199 -0.5228672
boats scomber 2015 14 1 1.171429e+01 9.143544e+00 2.443715e+00 8.750 1.2824434 1.4512182
boats scomber 2016 14 1 1.114286e+01 1.146806e+01 3.064969e+00 8.500 1.9450814 3.9102586
boats scomber 2017 14 1 8.142857e+00 9.130314e+00 2.440179e+00 6.500 1.7897980 2.5708407
boats scomber 2018 15 0 8.866667e+00 9.093692e+00 2.347981e+00 7.000 1.8095721 2.7197621
boats scomber 2019 15 0 8.666667e+00 9.060642e+00 2.339448e+00 7.500 1.7210566 2.4378993
boats trachurus 2009 15 0 1.253333e+01 7.150092e+00 1.846146e+00 6.500 0.9633239 0.7638070
boats trachurus 2010 16 0 1.162500e+01 9.344339e+00 2.336085e+00 11.750 0.6632156 -0.7731759
boats trachurus 2011 16 0 1.162500e+01 9.492980e+00 2.373245e+00 13.750 0.8521074 -0.0403181
boats trachurus 2012 15 1 1.213333e+01 9.796987e+00 2.529571e+00 8.000 1.2477990 0.8739410
boats trachurus 2013 14 2 1.135714e+01 8.571932e+00 2.290945e+00 10.500 1.1525445 0.2297956
boats trachurus 2014 14 1 1.307143e+01 1.056456e+01 2.823497e+00 15.000 0.6655238 -1.0268661
boats trachurus 2015 14 1 1.350000e+01 9.589658e+00 2.562944e+00 15.500 0.6154686 -0.5811107
boats trachurus 2016 15 0 1.300000e+01 1.174126e+01 3.031580e+00 17.000 0.8991196 -0.3774420
boats trachurus 2017 14 1 8.071429e+00 6.545278e+00 1.749299e+00 6.000 1.4426790 2.5368166
boats trachurus 2018 15 0 1.046667e+01 8.061076e+00 2.081361e+00 11.500 0.8842303 -0.6572651
boats trachurus 2019 15 0 9.800000e+00 8.537313e+00 2.204325e+00 9.000 1.6216490 2.7914741
catches anchovy 2009 9 6 3.271779e+04 5.711415e+04 1.903805e+04 47915.000 2.3755723 5.9670770
catches anchovy 2010 12 4 7.231761e+04 1.110272e+05 3.205079e+04 75940.125 1.9403697 3.4635021
catches anchovy 2011 11 5 8.872079e+04 1.233970e+05 3.720561e+04 103230.450 2.3953559 6.4456475
catches anchovy 2012 12 4 4.428604e+04 6.177484e+04 1.783286e+04 48774.750 1.7167673 1.7788491
catches anchovy 2013 13 3 1.650260e+05 2.555000e+05 7.086296e+04 204288.070 2.6052301 7.7256703
catches anchovy 2014 12 3 1.092608e+05 1.366068e+05 3.943497e+04 170798.200 1.0563017 -0.2147469
catches anchovy 2015 13 2 8.377939e+04 1.459626e+05 4.048274e+04 88097.410 2.5999876 7.2142986
catches anchovy 2016 14 1 8.413429e+04 1.852569e+05 4.951198e+04 36215.283 3.3439290 11.6529901
catches anchovy 2017 15 0 1.570535e+05 2.950314e+05 7.617678e+04 77693.315 2.5751941 6.6096435
catches anchovy 2018 15 0 1.523309e+05 2.662512e+05 6.874576e+04 142679.660 2.6443740 7.3720317
catches anchovy 2019 14 1 2.244497e+05 3.010431e+05 8.045715e+04 195464.445 1.6681372 1.9115519
catches bogue 2009 13 2 6.073062e+03 1.010286e+04 2.802030e+03 5185.400 2.3184932 5.5020830
catches bogue 2010 14 2 7.136618e+03 1.155602e+04 3.088477e+03 5063.238 2.4915004 6.5973279
catches bogue 2011 11 5 1.658992e+04 3.542016e+04 1.067958e+04 4322.250 3.0197391 9.3715996
catches bogue 2012 12 4 8.895208e+03 1.253582e+04 3.618779e+03 6304.812 2.2173892 4.8294957
catches bogue 2013 13 3 1.114880e+04 2.039712e+04 5.657143e+03 4158.000 1.9230391 2.3401075
catches bogue 2014 13 2 1.417109e+04 2.935248e+04 8.140914e+03 12074.900 3.2567305 11.1449996
catches bogue 2015 15 0 1.326532e+04 2.478812e+04 6.400265e+03 8169.325 2.5902441 6.9624542
catches bogue 2016 12 3 1.140874e+04 2.157469e+04 6.228075e+03 7211.180 2.8059969 8.2435757
catches bogue 2017 10 5 9.093106e+03 2.205912e+04 6.975706e+03 1964.390 3.1362668 9.8786099
catches bogue 2018 10 5 5.113137e+03 8.616058e+03 2.724637e+03 4449.382 2.7924634 8.2099286
catches bogue 2019 14 1 1.059815e+04 2.651832e+04 7.087319e+03 3351.540 3.4543790 12.3054308
catches sardine 2009 15 0 3.981417e+05 5.250735e+05 1.355734e+05 617351.525 1.7549157 3.4688100
catches sardine 2010 15 1 4.885023e+05 6.793598e+05 1.754099e+05 646399.250 1.8628849 3.7033143
catches sardine 2011 16 0 3.933311e+05 5.503656e+05 1.375914e+05 538073.637 1.8803255 3.5596384
catches sardine 2012 16 0 3.883538e+05 4.822951e+05 1.205738e+05 562885.050 1.3623090 1.0187110
catches sardine 2013 15 1 3.322331e+05 4.516674e+05 1.166200e+05 460418.950 1.8403145 3.5791453
catches sardine 2014 14 1 3.695708e+05 4.765313e+05 1.273583e+05 528014.120 2.1849648 5.7924458
catches sardine 2015 13 2 4.037188e+05 6.955548e+05 1.929122e+05 441037.520 3.0598037 10.1167443
catches sardine 2016 13 2 3.208298e+05 4.566522e+05 1.266525e+05 313510.080 2.5351409 7.2613537
catches sardine 2017 14 1 2.610419e+05 3.697470e+05 9.881903e+04 293002.300 1.6819347 2.0164894
catches sardine 2018 15 0 2.201219e+05 3.123330e+05 8.064404e+04 298253.635 2.2823250 6.1549544
catches sardine 2019 15 0 1.608340e+05 2.527359e+05 6.525612e+04 170653.530 2.2508711 5.2767388
catches sardinella 2009 12 3 1.240069e+05 1.253659e+05 3.619001e+04 173989.800 0.9697859 -0.1357467
catches sardinella 2010 13 3 9.211112e+04 1.199109e+05 3.325729e+04 126225.000 1.3621125 0.4346858
catches sardinella 2011 11 5 2.247909e+05 2.999612e+05 9.044170e+04 369289.250 1.1352738 -0.7661427
catches sardinella 2012 13 3 1.776686e+05 2.574445e+05 7.140227e+04 244174.000 1.7467660 2.3898352
catches sardinella 2013 14 2 8.466960e+04 1.424409e+05 3.806893e+04 60228.675 2.1009423 4.1491140
catches sardinella 2014 14 1 1.063287e+05 1.114812e+05 2.979460e+04 159191.318 0.9074250 -0.4446128
catches sardinella 2015 15 0 1.034683e+05 1.814381e+05 4.684711e+04 59993.030 2.3904001 5.2388590
catches sardinella 2016 14 1 7.056263e+04 1.091672e+05 2.917617e+04 62659.007 2.8699300 9.1036637
catches sardinella 2017 13 2 1.890293e+05 2.764203e+05 7.666519e+04 172000.780 1.7702547 2.2919011
catches sardinella 2018 15 0 1.298870e+05 1.378795e+05 3.560035e+04 159979.870 1.0727852 0.0221372
catches sardinella 2019 15 0 2.225944e+05 2.665683e+05 6.882765e+04 293028.185 1.2893362 0.3308980
catches scomber 2009 15 0 1.994332e+05 3.371167e+05 8.704315e+04 159308.375 2.6037452 6.9378327
catches scomber 2010 14 2 1.121223e+05 1.471586e+05 3.932980e+04 173554.872 1.5862827 2.0207205
catches scomber 2011 14 2 1.749267e+05 3.115144e+05 8.325572e+04 102926.075 2.4412503 5.8986868
catches scomber 2012 16 0 2.682738e+05 4.578142e+05 1.144535e+05 325571.143 2.6883923 8.0782043
catches scomber 2013 14 2 2.839468e+05 3.898576e+05 1.041938e+05 423940.847 1.2623756 0.1585591
catches scomber 2014 14 1 1.292620e+05 2.126440e+05 5.683150e+04 126329.212 2.0314188 3.2385689
catches scomber 2015 14 1 7.285417e+04 9.745453e+04 2.604582e+04 75504.232 2.4574360 7.1555113
catches scomber 2016 14 1 6.770949e+04 1.542892e+05 4.123553e+04 40232.565 3.5754357 13.0935863
catches scomber 2017 14 1 2.396674e+05 3.352111e+05 8.958893e+04 488675.638 1.0717677 -0.6556458
catches scomber 2018 15 0 1.274214e+05 2.754895e+05 7.113108e+04 88095.145 3.3688617 12.0162032
catches scomber 2019 15 0 1.584928e+05 2.774114e+05 7.162732e+04 192103.885 2.4597474 6.3233875
catches trachurus 2009 15 0 1.593702e+05 1.644160e+05 4.245202e+04 215551.575 1.0198565 -0.2695689
catches trachurus 2010 16 0 1.836991e+05 2.482350e+05 6.205874e+04 229830.962 1.7577813 2.7911513
catches trachurus 2011 16 0 1.523544e+05 1.647711e+05 4.119276e+04 216703.850 0.9893292 0.0157503
catches trachurus 2012 15 1 2.485976e+05 3.516572e+05 9.079749e+04 253879.925 2.8447802 9.4217049
catches trachurus 2013 14 2 1.857202e+05 2.451250e+05 6.551242e+04 242141.210 2.0350262 4.8069149
catches trachurus 2014 14 1 1.864060e+05 2.813704e+05 7.519940e+04 151953.755 2.1492326 4.4730089
catches trachurus 2015 14 1 1.654880e+05 2.194261e+05 5.864410e+04 224045.415 2.2379006 5.8118361
catches trachurus 2016 15 0 1.117214e+05 1.573678e+05 4.063218e+04 149989.945 2.3740101 6.4288282
catches trachurus 2017 14 1 1.376377e+05 2.015312e+05 5.386148e+04 183263.302 2.3156262 6.0009014
catches trachurus 2018 15 0 7.543053e+04 1.128198e+05 2.912995e+04 71776.695 2.8377129 9.1044784
catches trachurus 2019 15 0 6.576091e+04 8.667427e+04 2.237920e+04 65245.675 2.5510491 7.5158711
fishingdays anchovy 2009 9 6 8.744444e+01 1.293359e+02 4.311197e+01 79.000 1.6997204 1.9848623
fishingdays anchovy 2010 12 4 1.739167e+02 3.093716e+02 8.930790e+01 162.750 2.4172000 6.1109631
fishingdays anchovy 2011 11 5 1.636364e+02 2.485547e+02 7.494207e+01 162.500 2.3423229 5.8327214
fishingdays anchovy 2012 12 4 1.040000e+02 1.244479e+02 3.592501e+01 138.750 1.6889647 2.7572457
fishingdays anchovy 2013 13 3 1.887692e+02 3.161964e+02 8.769712e+01 214.000 2.9451706 9.5835620
fishingdays anchovy 2014 12 3 1.480833e+02 1.834668e+02 5.296232e+01 215.000 1.6462087 3.0900175
fishingdays anchovy 2015 13 2 1.910769e+02 3.368149e+02 9.341563e+01 202.000 2.6170096 7.3699319
fishingdays anchovy 2016 14 1 1.469286e+02 2.277733e+02 6.087498e+01 53.500 1.9675785 2.6059250
fishingdays anchovy 2017 15 0 1.802667e+02 2.929901e+02 7.564972e+01 122.000 2.0514905 3.2690526
fishingdays anchovy 2018 15 0 1.164000e+02 1.670465e+02 4.313123e+01 128.500 1.6866323 1.9269036
fishingdays anchovy 2019 14 1 2.216429e+02 2.809198e+02 7.507898e+01 274.500 1.5448224 1.5097749
fishingdays bogue 2009 13 2 3.123077e+01 5.323244e+01 1.476402e+01 41.000 2.8277691 8.8135131
fishingdays bogue 2010 14 2 3.135714e+01 5.432326e+01 1.451850e+01 35.250 3.0129887 9.9116554
fishingdays bogue 2011 11 5 5.172727e+01 7.069808e+01 2.131627e+01 34.500 2.6932382 8.0062788
fishingdays bogue 2012 12 4 4.208333e+01 6.031351e+01 1.741101e+01 32.500 2.9433855 9.3816740
fishingdays bogue 2013 13 3 4.353846e+01 9.742657e+01 2.702127e+01 15.000 3.3926467 11.8389322
fishingdays bogue 2014 13 2 6.623077e+01 1.784317e+02 4.948804e+01 24.000 3.5613890 12.7678816
fishingdays bogue 2015 15 0 5.926667e+01 1.547087e+02 3.994561e+01 29.000 3.8133016 14.6714710
fishingdays bogue 2016 12 3 5.358333e+01 1.157140e+02 3.340374e+01 27.000 3.3321547 11.3244378
fishingdays bogue 2017 10 5 5.560000e+01 1.375356e+02 4.349258e+01 8.750 3.1317479 9.8542769
fishingdays bogue 2018 10 5 2.540000e+01 5.009591e+01 1.584172e+01 10.750 3.0820522 9.6274886
fishingdays bogue 2019 14 1 2.164286e+01 4.185801e+01 1.118703e+01 11.250 2.9972378 9.3163547
fishingdays sardine 2009 15 0 3.152667e+02 3.574790e+02 9.230067e+01 438.000 1.3154841 1.4871621
fishingdays sardine 2010 15 1 4.056000e+02 5.213829e+02 1.346205e+02 660.000 2.0328304 5.0030744
fishingdays sardine 2011 16 0 3.577500e+02 4.709897e+02 1.177474e+02 517.500 2.0377169 4.9864674
fishingdays sardine 2012 16 0 3.755000e+02 4.126492e+02 1.031623e+02 512.250 1.2788795 1.0337218
fishingdays sardine 2013 15 1 3.617333e+02 4.441009e+02 1.146664e+02 560.500 1.8170332 3.7410382
fishingdays sardine 2014 14 1 4.130714e+02 4.931287e+02 1.317942e+02 357.000 2.0772492 4.7853700
fishingdays sardine 2015 13 2 4.343846e+02 5.595637e+02 1.551951e+02 347.000 2.6291034 8.1201465
fishingdays sardine 2016 13 2 3.766154e+02 4.483632e+02 1.243536e+02 292.000 2.3790909 6.8393429
fishingdays sardine 2017 14 1 3.157857e+02 3.626410e+02 9.691988e+01 362.000 1.5816902 2.1753716
fishingdays sardine 2018 15 0 2.315333e+02 2.842835e+02 7.340169e+01 334.000 1.9039807 4.4476888
fishingdays sardine 2019 15 0 2.247333e+02 2.822971e+02 7.288879e+01 348.500 1.8730290 4.0065248
fishingdays sardinella 2009 12 3 6.658333e+01 7.725337e+01 2.230113e+01 70.000 2.0327083 4.9497530
fishingdays sardinella 2010 13 3 4.807692e+01 6.770458e+01 1.877787e+01 47.000 2.0265176 3.6352702
fishingdays sardinella 2011 11 5 1.258182e+02 1.962329e+02 5.916646e+01 123.500 1.8813752 2.8647326
fishingdays sardinella 2012 13 3 1.050000e+02 1.643969e+02 4.559549e+01 80.000 2.2616052 5.2456893
fishingdays sardinella 2013 14 2 5.578571e+01 9.372150e+01 2.504812e+01 36.500 2.0833415 3.1167765
fishingdays sardinella 2014 14 1 1.025714e+02 1.261743e+02 3.372150e+01 104.500 1.7594756 2.6676632
fishingdays sardinella 2015 15 0 1.036667e+02 1.660308e+02 4.286898e+01 47.500 2.5397225 6.0681342
fishingdays sardinella 2016 14 1 5.800000e+01 8.842380e+01 2.363226e+01 43.500 2.0861104 3.2034763
fishingdays sardinella 2017 13 2 1.075385e+02 1.673278e+02 4.640839e+01 80.000 2.2106583 4.7795136
fishingdays sardinella 2018 15 0 6.906667e+01 8.593724e+01 2.218890e+01 76.000 1.7158014 2.2922636
fishingdays sardinella 2019 15 0 9.413333e+01 1.218142e+02 3.145229e+01 74.500 1.8072294 2.2206516
fishingdays scomber 2009 15 0 2.080000e+02 2.300602e+02 5.940130e+01 232.000 1.7886812 3.3992839
fishingdays scomber 2010 14 2 1.847143e+02 1.908045e+02 5.099466e+01 252.500 1.0208371 0.2453498
fishingdays scomber 2011 14 2 2.395714e+02 2.693110e+02 7.197639e+01 276.000 1.8060906 3.5939219
fishingdays scomber 2012 16 0 2.745000e+02 3.589893e+02 8.974733e+01 334.000 2.3009772 6.2964066
fishingdays scomber 2013 14 2 2.769286e+02 3.540662e+02 9.462816e+01 327.000 1.8388974 3.3788996
fishingdays scomber 2014 14 1 2.352857e+02 3.560215e+02 9.515074e+01 170.250 2.2687922 4.7030379
fishingdays scomber 2015 14 1 2.037857e+02 2.484742e+02 6.640751e+01 251.750 2.1947418 5.6681386
fishingdays scomber 2016 14 1 1.350000e+02 1.531756e+02 4.093791e+01 133.750 1.3992699 0.9034565
fishingdays scomber 2017 14 1 1.827143e+02 2.712536e+02 7.249558e+01 254.250 2.5086537 7.2694536
fishingdays scomber 2018 15 0 1.168667e+02 1.682362e+02 4.343839e+01 121.000 2.7880160 8.8495865
fishingdays scomber 2019 15 0 1.210667e+02 1.611412e+02 4.160649e+01 97.000 2.4229094 6.3663354
fishingdays trachurus 2009 15 0 3.328667e+02 3.232000e+02 8.344989e+01 323.000 1.3618401 1.4993938
fishingdays trachurus 2010 16 0 2.893125e+02 2.981080e+02 7.452699e+01 374.750 1.1862607 0.9080592
fishingdays trachurus 2011 16 0 3.283750e+02 3.346022e+02 8.365056e+01 377.750 1.5735461 2.8514455
fishingdays trachurus 2012 15 1 3.575333e+02 4.022946e+02 1.038720e+02 400.000 2.2755367 6.2440556
fishingdays trachurus 2013 14 2 3.187143e+02 3.963224e+02 1.059216e+02 278.500 2.1892939 5.2773570
fishingdays trachurus 2014 14 1 3.537857e+02 4.522770e+02 1.208761e+02 213.000 2.0051720 3.4957530
fishingdays trachurus 2015 14 1 3.195000e+02 3.380771e+02 9.035490e+01 287.500 1.8916937 3.9273739
fishingdays trachurus 2016 15 0 2.232000e+02 2.493418e+02 6.437977e+01 167.000 1.8339848 2.6839357
fishingdays trachurus 2017 14 1 3.270000e+02 4.126751e+02 1.102921e+02 306.000 1.9174364 3.9252841
fishingdays trachurus 2018 15 0 1.860667e+02 1.980698e+02 5.114140e+01 151.000 1.5890431 2.0832502
fishingdays trachurus 2019 15 0 1.776667e+02 1.979115e+02 5.110052e+01 154.500 2.2111537 5.5968436
year anchovy 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year anchovy 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year bogue 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardine 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year sardinella 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year scomber 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2009 15 0 2.009000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2010 16 0 2.010000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2011 16 0 2.011000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2012 16 0 2.012000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2013 16 0 2.013000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2014 15 0 2.014000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2015 15 0 2.015000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2016 15 0 2.016000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2017 15 0 2.017000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2018 15 0 2.018000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
year trachurus 2019 15 0 2.019000e+03 0.000000e+00 0.000000e+00 0.000 NaN NaN
Table 3. Northern Spain GSA-06: Variables description per species and year
described_variables species year n na mean sd se_mean IQR skewness kurtosis
boats anchovy 2009 20 0 3.090000e+01 2.013873e+01 4.503157e+00 31.0000 0.0854876 -0.9909960
boats anchovy 2010 19 1 3.168421e+01 1.806486e+01 4.144363e+00 20.5000 0.0591874 -0.8049967
boats anchovy 2011 19 2 2.678947e+01 1.363239e+01 3.127485e+00 17.0000 -0.1888889 -0.3559738
boats anchovy 2012 19 0 2.621053e+01 1.472556e+01 3.378275e+00 21.5000 0.2162847 -0.5047410
boats anchovy 2013 19 1 2.731579e+01 1.484757e+01 3.406266e+00 13.0000 0.6889913 0.5916654
boats anchovy 2014 20 0 2.845000e+01 1.634649e+01 3.655187e+00 16.7500 0.3979485 -0.2627076
boats anchovy 2015 19 0 2.500000e+01 1.154219e+01 2.647961e+00 17.0000 -0.2219289 -1.0608282
boats anchovy 2016 20 0 2.845000e+01 1.716629e+01 3.838500e+00 17.2500 0.7073209 -0.2539749
boats anchovy 2017 21 0 2.347619e+01 1.752318e+01 3.823872e+00 22.0000 0.9534480 0.6919442
boats anchovy 2018 24 0 2.291667e+01 2.079698e+01 4.245166e+00 18.0000 0.9900892 -0.1357662
boats anchovy 2019 26 0 1.876923e+01 1.627343e+01 3.191483e+00 26.7500 0.5256565 -0.9110914
boats bogue 2009 15 5 6.333333e+00 4.623954e+00 1.193900e+00 6.5000 0.8695807 0.3428749
boats bogue 2010 15 5 5.933333e+00 4.963678e+00 1.281616e+00 3.5000 1.5419259 1.6677344
boats bogue 2011 14 7 6.571429e+00 6.629935e+00 1.771925e+00 7.5000 1.4885514 1.8016449
boats bogue 2012 17 2 4.823529e+00 4.231361e+00 1.026256e+00 5.0000 1.4517784 1.8819847
boats bogue 2013 14 6 3.857143e+00 2.178819e+00 5.823139e-01 3.2500 0.5248341 -0.6207737
boats bogue 2014 14 6 3.071429e+00 1.859044e+00 4.968504e-01 2.0000 0.6336916 -0.9448249
boats bogue 2015 13 6 2.923077e+00 2.289889e+00 6.351008e-01 4.0000 1.1936275 0.3328824
boats bogue 2016 11 9 2.363636e+00 1.747726e+00 5.269592e-01 3.0000 0.6902394 -1.5784439
boats bogue 2017 10 11 1.900000e+00 9.944289e-01 3.144660e-01 1.0000 1.0846946 0.9138457
boats bogue 2018 10 14 2.000000e+00 1.414214e+00 4.472136e-01 1.0000 1.4731391 1.2261905
boats bogue 2019 12 14 2.166667e+00 1.337116e+00 3.859921e-01 2.0000 1.0090020 0.2047687
boats sardine 2009 18 2 3.322222e+01 1.720313e+01 4.054817e+00 26.0000 -0.0378541 -0.7703992
boats sardine 2010 18 2 3.311111e+01 1.595172e+01 3.759857e+00 20.5000 -0.1352856 -0.8907324
boats sardine 2011 20 1 2.630000e+01 1.521806e+01 3.402863e+00 24.0000 -0.1381987 -0.8704276
boats sardine 2012 19 0 2.542105e+01 1.381350e+01 3.169035e+00 22.5000 0.2111676 -0.4689361
boats sardine 2013 19 1 2.594737e+01 1.262922e+01 2.897341e+00 14.0000 0.1130757 -0.6083744
boats sardine 2014 19 1 2.831579e+01 1.503719e+01 3.449767e+00 16.5000 0.6176624 -0.4690743
boats sardine 2015 19 0 2.094737e+01 1.007458e+01 2.311266e+00 7.5000 0.1661053 0.0439285
boats sardine 2016 20 0 2.570000e+01 1.502664e+01 3.360060e+00 17.5000 0.6849591 -0.1716424
boats sardine 2017 21 0 2.109524e+01 1.692308e+01 3.692918e+00 22.0000 1.1593941 1.3775438
boats sardine 2018 23 1 2.208696e+01 1.945604e+01 4.056865e+00 17.0000 0.9634591 -0.1746043
boats sardine 2019 24 2 1.970833e+01 1.438139e+01 2.935588e+00 22.7500 0.2316979 -0.9709395
boats sardinella 2009 18 2 1.044444e+01 6.792922e+00 1.601107e+00 6.5000 1.0802049 1.3403272
boats sardinella 2010 17 3 1.376471e+01 7.734092e+00 1.875793e+00 9.0000 0.7496054 -0.0482701
boats sardinella 2011 17 4 1.529412e+01 1.122366e+01 2.722138e+00 15.0000 1.2217359 1.2408417
boats sardinella 2012 18 1 1.361111e+01 1.033064e+01 2.434956e+00 17.5000 0.6510579 -1.3000222
boats sardinella 2013 17 3 9.941176e+00 1.031668e+01 2.502162e+00 9.0000 2.0687627 3.7719033
boats sardinella 2014 19 1 7.894737e+00 6.349932e+00 1.456774e+00 8.0000 1.5901739 3.3772777
boats sardinella 2015 18 1 1.372222e+01 8.490864e+00 2.001316e+00 12.2500 0.5438496 0.2243039
boats sardinella 2016 19 1 1.100000e+01 9.249625e+00 2.122009e+00 11.5000 1.4852873 2.3318941
boats sardinella 2017 20 1 1.120000e+01 9.616871e+00 2.150398e+00 13.0000 1.3065727 1.3842864
boats sardinella 2018 18 6 1.050000e+01 8.147537e+00 1.920393e+00 10.5000 1.0028283 0.4917885
boats sardinella 2019 19 7 1.026316e+01 8.129795e+00 1.865103e+00 8.5000 1.2343058 1.5403000
boats scomber 2009 20 0 2.110000e+01 1.504345e+01 3.363817e+00 25.7500 0.3690094 -1.2305517
boats scomber 2010 19 1 2.089474e+01 1.269687e+01 2.912862e+00 18.0000 0.3358791 -0.6719440
boats scomber 2011 20 1 1.625000e+01 1.068632e+01 2.389533e+00 16.0000 0.4676724 -0.1486544
boats scomber 2012 19 0 1.378947e+01 7.261611e+00 1.665928e+00 12.5000 0.1248027 -1.3027962
boats scomber 2013 19 1 1.668421e+01 9.781233e+00 2.243969e+00 11.0000 1.1131188 1.2126147
boats scomber 2014 19 1 1.800000e+01 9.860133e+00 2.262070e+00 12.0000 0.7372291 0.3951650
boats scomber 2015 19 0 1.289474e+01 8.089051e+00 1.855756e+00 9.5000 0.7697908 1.3815031
boats scomber 2016 20 0 1.355000e+01 1.050050e+01 2.347983e+00 11.7500 1.2762259 1.6414502
boats scomber 2017 19 2 1.463158e+01 1.261926e+01 2.895056e+00 14.5000 1.2471098 1.0102990
boats scomber 2018 21 3 1.204762e+01 1.019057e+01 2.223764e+00 12.0000 0.8831680 -0.3327328
boats scomber 2019 19 7 1.231579e+01 8.596205e+00 1.972105e+00 11.5000 0.8550392 0.3520648
boats trachurus 2009 19 1 1.852632e+01 1.209393e+01 2.774538e+00 22.5000 0.2222745 -1.4781549
boats trachurus 2010 20 0 1.595000e+01 1.146379e+01 2.563381e+00 17.5000 0.5320051 -0.6782463
boats trachurus 2011 20 1 1.405000e+01 1.174947e+01 2.627261e+00 15.5000 0.9080313 -0.4133574
boats trachurus 2012 19 0 1.257895e+01 8.865136e+00 2.033802e+00 12.5000 0.6655779 -0.8916866
boats trachurus 2013 20 0 1.150000e+01 7.796761e+00 1.743409e+00 8.0000 1.1034294 0.5908003
boats trachurus 2014 20 0 1.320000e+01 9.064680e+00 2.026924e+00 9.7500 0.8406576 0.8345519
boats trachurus 2015 18 1 1.261111e+01 8.984555e+00 2.117680e+00 9.7500 1.3483470 1.5328337
boats trachurus 2016 19 1 1.242105e+01 1.021609e+01 2.343731e+00 7.5000 1.8883955 4.3894372
boats trachurus 2017 19 2 1.021053e+01 9.390178e+00 2.154254e+00 7.5000 1.6389648 2.6200150
boats trachurus 2018 19 5 1.194737e+01 8.978206e+00 2.059742e+00 13.0000 0.6748743 -0.8584104
boats trachurus 2019 21 5 9.809524e+00 7.487450e+00 1.633895e+00 13.0000 0.6905056 -0.7400422
catches anchovy 2009 20 0 5.937057e+05 5.581020e+05 1.247954e+05 359603.0000 1.2361497 0.1472612
catches anchovy 2010 19 1 5.204099e+05 3.602555e+05 8.264828e+04 339560.2500 0.9821157 0.5200600
catches anchovy 2011 19 2 4.981508e+05 3.455128e+05 7.926608e+04 449578.6500 0.7768577 0.0023472
catches anchovy 2012 19 0 6.017844e+05 4.017074e+05 9.215800e+04 344403.8750 1.3510748 2.4910295
catches anchovy 2013 19 1 9.025713e+05 6.023025e+05 1.381777e+05 426807.0500 1.4438453 1.0538446
catches anchovy 2014 20 0 8.497339e+05 6.673094e+05 1.492149e+05 742701.6975 1.1340549 0.9296280
catches anchovy 2015 19 0 8.748889e+05 6.857353e+05 1.573185e+05 801207.3700 1.2370068 1.6335006
catches anchovy 2016 20 0 8.750867e+05 6.425629e+05 1.436814e+05 989316.7750 0.6118494 -0.1030078
catches anchovy 2017 21 0 8.686628e+05 7.063866e+05 1.541462e+05 968503.3000 0.7157145 -0.3085214
catches anchovy 2018 24 0 8.885224e+05 8.220443e+05 1.677991e+05 1108934.4875 1.1185366 1.1890486
catches anchovy 2019 26 0 5.355870e+05 6.078964e+05 1.192183e+05 599359.0025 1.5399615 2.1617796
catches bogue 2009 15 5 5.245429e+03 8.331887e+03 2.151284e+03 5830.0000 2.8617233 9.2351960
catches bogue 2010 15 5 2.652940e+03 3.548567e+03 9.162360e+02 3467.3500 2.1416140 5.0726932
catches bogue 2011 14 7 6.562012e+03 1.063813e+04 2.843159e+03 6085.2000 2.0658353 3.3731347
catches bogue 2012 17 2 1.684571e+03 2.057112e+03 4.989229e+02 1334.0000 1.5974521 1.7689975
catches bogue 2013 14 6 1.833125e+03 1.736414e+03 4.640763e+02 2446.2500 0.9726007 -0.1110142
catches bogue 2014 14 6 1.228174e+03 1.930751e+03 5.160150e+02 1414.5775 2.6806408 8.0799046
catches bogue 2015 13 6 8.662508e+02 1.222175e+03 3.389705e+02 1370.0000 1.8856155 3.7086906
catches bogue 2016 11 9 1.362162e+03 2.394303e+03 7.219095e+02 1188.5050 2.2889508 5.2623839
catches bogue 2017 10 11 5.114390e+02 5.772862e+02 1.825539e+02 900.8325 0.8587957 -0.9120881
catches bogue 2018 10 14 4.325640e+02 3.680003e+02 1.163719e+02 638.6025 0.2390136 -1.9884062
catches bogue 2019 12 14 1.680237e+03 1.646865e+03 4.754091e+02 1957.4625 1.0561999 0.3831373
catches sardine 2009 18 2 4.914641e+05 3.033437e+05 7.149879e+04 362480.5000 0.7660380 -0.2563525
catches sardine 2010 18 2 4.863473e+05 2.515364e+05 5.928771e+04 312575.1725 1.1248903 1.6647280
catches sardine 2011 20 1 6.060972e+05 4.244576e+05 9.491161e+04 557383.7500 0.5668977 0.2790181
catches sardine 2012 19 0 4.838890e+05 3.118152e+05 7.153530e+04 519240.3500 0.2818368 -1.0149530
catches sardine 2013 19 1 5.120265e+05 3.070442e+05 7.044077e+04 400147.5250 0.6837149 0.3488687
catches sardine 2014 19 1 5.119726e+05 3.357040e+05 7.701578e+04 645358.1550 0.4405163 -1.2751621
catches sardine 2015 19 0 3.329651e+05 3.027329e+05 6.945169e+04 309657.0850 1.0813117 0.3030559
catches sardine 2016 20 0 4.967104e+05 4.323450e+05 9.667528e+04 475816.0075 0.8856771 -0.2669866
catches sardine 2017 21 0 3.416447e+05 3.168701e+05 6.914673e+04 457716.9700 0.8809778 -0.2664000
catches sardine 2018 23 1 3.620744e+05 3.285278e+05 6.850278e+04 451067.2900 0.7292631 -0.4098432
catches sardine 2019 24 2 2.796344e+05 3.374199e+05 6.887554e+04 321982.1275 1.6432874 2.2119134
catches sardinella 2009 18 2 6.233701e+04 7.947546e+04 1.873255e+04 53224.7500 2.0729211 4.1832920
catches sardinella 2010 17 3 1.600942e+05 1.505327e+05 3.650955e+04 184128.0000 1.3940741 2.0258035
catches sardinella 2011 17 4 2.939935e+05 4.686544e+05 1.136654e+05 263506.0000 2.2525136 4.6803287
catches sardinella 2012 18 1 2.094472e+05 2.850549e+05 6.718808e+04 223765.7500 2.2024568 5.7363528
catches sardinella 2013 17 3 2.022685e+05 4.010929e+05 9.727933e+04 105332.0000 2.4315262 5.3670275
catches sardinella 2014 19 1 7.664252e+04 1.708423e+05 3.919391e+04 15987.4000 2.7179696 6.6884749
catches sardinella 2015 18 1 1.315686e+05 1.232248e+05 2.904436e+04 151501.1125 1.7876023 4.1707978
catches sardinella 2016 19 1 5.758044e+04 8.275120e+04 1.898443e+04 63004.8200 2.8647211 9.7216884
catches sardinella 2017 20 1 1.222413e+05 1.569107e+05 3.508630e+04 125679.7575 2.1105357 5.1281009
catches sardinella 2018 18 6 9.882850e+04 9.540139e+04 2.248632e+04 130283.1575 1.0057765 0.1893774
catches sardinella 2019 19 7 8.135932e+04 8.282181e+04 1.900063e+04 88616.3850 1.3544855 0.9794500
catches scomber 2009 20 0 6.597630e+04 1.011468e+05 2.261711e+04 73330.0375 3.4307269 13.4818046
catches scomber 2010 19 1 6.457944e+04 8.609458e+04 1.975145e+04 68264.0500 1.9461470 3.3764622
catches scomber 2011 20 1 3.709295e+04 4.306634e+04 9.629926e+03 56526.0600 1.3983237 1.0678389
catches scomber 2012 19 0 2.751224e+04 3.411401e+04 7.826291e+03 32815.3000 1.5076883 1.1768864
catches scomber 2013 19 1 4.526330e+04 4.812106e+04 1.103973e+04 66066.4500 1.2811688 0.6636847
catches scomber 2014 19 1 4.498960e+04 5.049346e+04 1.158399e+04 48938.2050 1.7085378 2.4011803
catches scomber 2015 19 0 2.622454e+04 2.985075e+04 6.848231e+03 41535.2550 1.0879893 -0.4293950
catches scomber 2016 20 0 2.894550e+04 4.614818e+04 1.031905e+04 20280.2325 2.9097765 9.6782832
catches scomber 2017 19 2 6.501633e+04 1.421984e+05 3.262256e+04 41546.1050 3.6097924 13.8042442
catches scomber 2018 21 3 2.781355e+04 3.273346e+04 7.143026e+03 25748.7400 1.4537569 1.0508007
catches scomber 2019 19 7 3.167948e+04 4.273518e+04 9.804124e+03 48626.3350 1.5595774 1.6439687
catches trachurus 2009 19 1 7.646750e+04 8.714743e+04 1.999299e+04 86661.9750 1.9770095 4.5266604
catches trachurus 2010 20 0 4.574532e+04 6.376606e+04 1.425852e+04 46561.2425 2.1170748 4.3817332
catches trachurus 2011 20 1 5.594667e+04 7.033464e+04 1.572730e+04 85250.0750 1.2111321 0.2454947
catches trachurus 2012 19 0 2.780194e+04 3.385104e+04 7.765961e+03 28782.1650 1.7060414 2.5750210
catches trachurus 2013 20 0 1.956605e+04 2.081169e+04 4.653636e+03 27720.0125 1.0286898 -0.4586612
catches trachurus 2014 20 0 1.847588e+04 2.296642e+04 5.135447e+03 21352.4625 1.5713989 1.6487304
catches trachurus 2015 18 1 1.483685e+04 1.419206e+04 3.345101e+03 19942.0450 0.9595656 -0.4018047
catches trachurus 2016 19 1 1.206538e+04 1.245834e+04 2.858140e+03 18821.5550 1.0779327 -0.0639037
catches trachurus 2017 19 2 9.372046e+03 1.060264e+04 2.432411e+03 8766.2450 1.2276621 -0.0362870
catches trachurus 2018 19 5 1.229795e+04 1.117437e+04 2.563577e+03 15343.5450 0.7324115 -0.7944523
catches trachurus 2019 21 5 1.701417e+04 2.382701e+04 5.199480e+03 18964.9500 1.8984068 2.9175993
fishingdays anchovy 2009 20 0 4.893000e+02 4.427605e+02 9.900426e+01 250.0000 1.4595818 1.4080391
fishingdays anchovy 2010 19 1 5.208421e+02 3.695606e+02 8.478301e+01 315.0000 1.7602094 4.3250851
fishingdays anchovy 2011 19 2 5.246316e+02 3.394543e+02 7.787617e+01 302.0000 1.1112788 1.8205188
fishingdays anchovy 2012 19 0 6.346316e+02 4.217868e+02 9.676454e+01 397.5000 1.6091592 4.1123308
fishingdays anchovy 2013 19 1 6.859474e+02 4.143937e+02 9.506844e+01 337.5000 1.3128328 0.7361170
fishingdays anchovy 2014 20 0 7.386500e+02 5.502095e+02 1.230306e+02 682.7500 0.9834804 0.5191623
fishingdays anchovy 2015 19 0 7.451053e+02 5.600612e+02 1.284869e+02 639.5000 1.1853865 1.1907210
fishingdays anchovy 2016 20 0 7.537000e+02 5.722849e+02 1.279668e+02 706.0000 0.6956644 -0.5769653
fishingdays anchovy 2017 21 0 6.507143e+02 5.264076e+02 1.148715e+02 757.0000 0.7187064 -0.5410817
fishingdays anchovy 2018 24 0 5.254167e+02 4.519797e+02 9.225997e+01 708.5000 0.8732727 0.8195947
fishingdays anchovy 2019 26 0 4.086923e+02 4.175598e+02 8.189022e+01 578.2500 1.1028708 0.7106738
fishingdays bogue 2009 15 5 2.046667e+01 2.125648e+01 5.488401e+00 23.5000 1.2234788 0.4703358
fishingdays bogue 2010 15 5 1.860000e+01 2.305831e+01 5.953630e+00 13.5000 2.0279486 3.8495707
fishingdays bogue 2011 14 7 2.878571e+01 3.814986e+01 1.019598e+01 30.2500 1.6198397 1.8111894
fishingdays bogue 2012 17 2 1.217647e+01 1.425322e+01 3.456915e+00 17.0000 1.6520425 2.5652829
fishingdays bogue 2013 14 6 1.335714e+01 1.216304e+01 3.250709e+00 13.5000 1.2344779 0.6033854
fishingdays bogue 2014 14 6 1.242857e+01 2.172455e+01 5.806130e+00 9.7500 3.1434271 10.6328480
fishingdays bogue 2015 13 6 8.153846e+00 8.849163e+00 2.454316e+00 12.0000 1.0480465 -0.5950163
fishingdays bogue 2016 11 9 8.818182e+00 1.009770e+01 3.044573e+00 11.0000 1.9518344 4.4048627
fishingdays bogue 2017 10 11 4.600000e+00 4.060651e+00 1.284091e+00 6.5000 0.8851633 -1.2422573
fishingdays bogue 2018 10 14 4.400000e+00 4.452215e+00 1.407914e+00 4.5000 1.4246951 1.2065044
fishingdays bogue 2019 12 14 6.250000e+00 5.412528e+00 1.562462e+00 5.7500 1.4992372 3.0397512
fishingdays sardine 2009 18 2 6.314444e+02 3.839270e+02 9.049245e+01 468.5000 0.9449967 0.3489927
fishingdays sardine 2010 18 2 5.454444e+02 2.795438e+02 6.588910e+01 303.0000 1.0609533 0.6205225
fishingdays sardine 2011 20 1 5.781000e+02 4.152807e+02 9.285960e+01 423.2500 1.1531177 2.6947956
fishingdays sardine 2012 19 0 5.933158e+02 4.008698e+02 9.196583e+01 414.5000 1.2591390 2.0456702
fishingdays sardine 2013 19 1 5.964211e+02 3.265711e+02 7.492055e+01 361.5000 0.7851266 -0.0612880
fishingdays sardine 2014 19 1 6.875789e+02 4.208774e+02 9.655590e+01 489.5000 0.6936581 -0.5377355
fishingdays sardine 2015 19 0 5.486316e+02 4.254216e+02 9.759842e+01 617.5000 0.8861359 0.5257696
fishingdays sardine 2016 20 0 6.420000e+02 4.978682e+02 1.113267e+02 662.0000 0.6405713 -0.5939214
fishingdays sardine 2017 21 0 4.877619e+02 4.224325e+02 9.218232e+01 584.0000 0.7281994 -0.6000047
fishingdays sardine 2018 23 1 4.133478e+02 3.482906e+02 7.262361e+01 506.5000 0.7177868 0.1237290
fishingdays sardine 2019 24 2 3.912917e+02 3.692738e+02 7.537771e+01 512.0000 1.0738222 0.6391260
fishingdays sardinella 2009 18 2 3.972222e+01 4.758992e+01 1.121705e+01 37.2500 3.0407484 10.9074849
fishingdays sardinella 2010 17 3 7.470588e+01 6.435717e+01 1.560891e+01 75.0000 1.9381877 4.9544936
fishingdays sardinella 2011 17 4 1.147647e+02 1.551058e+02 3.761869e+01 116.0000 2.2587408 5.0689189
fishingdays sardinella 2012 18 1 9.583333e+01 1.070466e+02 2.523112e+01 125.5000 2.0601318 5.2713463
fishingdays sardinella 2013 17 3 5.870588e+01 9.373418e+01 2.273388e+01 44.0000 2.2816313 4.6224297
fishingdays sardinella 2014 19 1 4.231579e+01 7.138865e+01 1.637768e+01 35.0000 2.5471739 5.6593821
fishingdays sardinella 2015 18 1 9.450000e+01 1.148509e+02 2.707062e+01 71.2500 2.8402032 9.4012984
fishingdays sardinella 2016 19 1 4.010526e+01 3.973788e+01 9.116496e+00 45.0000 1.2153654 0.5824070
fishingdays sardinella 2017 20 1 6.100000e+01 5.678584e+01 1.269770e+01 81.5000 0.8808499 -0.2335488
fishingdays sardinella 2018 18 6 5.444444e+01 4.467032e+01 1.052890e+01 67.7500 0.6766708 -0.5397922
fishingdays sardinella 2019 19 7 5.605263e+01 6.518561e+01 1.495460e+01 54.5000 2.5489503 7.8289475
fishingdays scomber 2009 20 0 1.454500e+02 1.314404e+02 2.939096e+01 163.5000 1.2333598 1.5898427
fishingdays scomber 2010 19 1 1.426316e+02 1.124862e+02 2.580609e+01 123.5000 1.4565433 2.5097344
fishingdays scomber 2011 20 1 1.297500e+02 1.318703e+02 2.948710e+01 163.7500 1.2673559 0.4484231
fishingdays scomber 2012 19 0 9.205263e+01 7.610992e+01 1.746081e+01 118.5000 0.7985400 -0.6730573
fishingdays scomber 2013 19 1 1.231053e+02 7.613212e+01 1.746591e+01 115.5000 0.6308431 -0.5025632
fishingdays scomber 2014 19 1 1.595789e+02 1.039029e+02 2.383696e+01 156.5000 0.5739461 -0.7087503
fishingdays scomber 2015 19 0 9.778947e+01 8.438640e+01 1.935957e+01 86.0000 1.2132615 0.6942449
fishingdays scomber 2016 20 0 8.245000e+01 7.054635e+01 1.577464e+01 91.0000 1.0426688 0.4395519
fishingdays scomber 2017 19 2 1.204211e+02 1.101233e+02 2.526402e+01 134.5000 1.0955063 0.6498738
fishingdays scomber 2018 21 3 7.804762e+01 6.767900e+01 1.476877e+01 64.0000 1.2395817 1.2224064
fishingdays scomber 2019 19 7 8.589474e+01 8.106917e+01 1.859854e+01 84.5000 1.5093690 1.9061754
fishingdays trachurus 2009 19 1 1.762632e+02 1.782392e+02 4.089087e+01 181.0000 1.5285036 1.9592025
fishingdays trachurus 2010 20 0 1.208500e+02 1.379093e+02 3.083746e+01 151.0000 1.4676683 1.3052196
fishingdays trachurus 2011 20 1 1.360000e+02 1.671306e+02 3.737153e+01 138.7500 1.4096451 0.6297898
fishingdays trachurus 2012 19 0 9.542105e+01 1.091107e+02 2.503171e+01 96.5000 1.9988809 3.9888011
fishingdays trachurus 2013 20 0 7.290000e+01 6.750431e+01 1.509442e+01 74.0000 1.2810857 1.1854762
fishingdays trachurus 2014 20 0 8.550000e+01 8.184485e+01 1.830106e+01 91.7500 1.4110476 1.7970774
fishingdays trachurus 2015 18 1 1.019444e+02 1.079861e+02 2.545257e+01 113.0000 1.4284887 1.0774073
fishingdays trachurus 2016 19 1 7.715789e+01 7.759458e+01 1.780142e+01 86.0000 1.5082841 1.9216180
fishingdays trachurus 2017 19 2 6.878947e+01 8.948034e+01 2.052820e+01 49.5000 2.1608420 4.0089452
fishingdays trachurus 2018 19 5 8.321053e+01 7.665984e+01 1.758697e+01 68.5000 1.2891906 0.7427631
fishingdays trachurus 2019 21 5 6.366667e+01 7.270786e+01 1.586616e+01 89.0000 1.9881743 4.8074347
year anchovy 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year anchovy 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year bogue 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardine 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year sardinella 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year scomber 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2009 20 0 2.009000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2010 20 0 2.010000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2011 21 0 2.011000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2012 19 0 2.012000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2013 20 0 2.013000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2014 20 0 2.014000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2015 19 0 2.015000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2016 20 0 2.016000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2017 21 0 2.017000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2018 24 0 2.018000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN
year trachurus 2019 26 0 2.019000e+03 0.000000e+00 0.000000e+00 0.0000 NaN NaN

In the variables description table, NA data are detected. The next graphic shows the percentage of NA in the different variables. NA data are excluded in each dataset.

After deleting NA data, the dataset for fish zone Alboran Sea GSA-01 has a dimension of n=109 x p=22 with fishing data from 16 harbours. The dataset for fish zone Northern Spain GSA-06 has a dimension of n=140 x p=22 with fishing data from 28 harbours.

2.1.1 ALBORAN SEA GSA-01

The function summary() offers the principal statistical descriptors for the variables of each species and year.The function ggpairs() from ‘GGally’ was used to visialuzar the relationship between variables.

In this analysis, NA data are excluded.

Table 4. Statistical descriptors for anchovy in Alboran Sea GSA-01
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2009 Min. : 28 Min. : 1.00 Min. : 1.00
1st Qu.:2009 1st Qu.: 1020 1st Qu.: 7.00 1st Qu.: 4.00
Median :2009 Median : 5625 Median : 28.00 Median : 9.00
Mean :2009 Mean : 32718 Mean : 87.44 Mean :10.56
3rd Qu.:2009 3rd Qu.: 48935 3rd Qu.: 86.00 3rd Qu.:11.00
Max. :2009 Max. :175341 Max. :367.00 Max. :27.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2010 Min. : 22 Min. : 1.0 Min. : 1.00
1st Qu.:2010 1st Qu.: 4690 1st Qu.: 7.5 1st Qu.: 3.00
Median :2010 Median : 15528 Median : 35.5 Median : 9.50
Mean :2010 Mean : 72318 Mean : 173.9 Mean :12.58
3rd Qu.:2010 3rd Qu.: 80631 3rd Qu.: 170.2 3rd Qu.:17.25
Max. :2010 Max. :358024 Max. :1046.0 Max. :34.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2011 Min. : 420 Min. : 2.0 Min. : 1.00
1st Qu.:2011 1st Qu.: 9034 1st Qu.: 24.0 1st Qu.: 6.00
Median :2011 Median : 43815 Median : 70.0 Median :11.00
Mean :2011 Mean : 88721 Mean :163.6 Mean :12.82
3rd Qu.:2011 3rd Qu.:112265 3rd Qu.:186.5 3rd Qu.:21.00
Max. :2011 Max. :427752 Max. :836.0 Max. :28.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2012 Min. : 1582 Min. : 2.00 Min. : 1.00
1st Qu.:2012 1st Qu.: 6689 1st Qu.: 29.75 1st Qu.: 5.75
Median :2012 Median : 16018 Median : 43.00 Median :10.00
Mean :2012 Mean : 44286 Mean :104.00 Mean :13.00
3rd Qu.:2012 3rd Qu.: 55464 3rd Qu.:168.50 3rd Qu.:22.25
Max. :2012 Max. :181050 Max. :418.00 Max. :29.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2013 Min. : 26 Min. : 1.0 Min. : 1.00
1st Qu.:2013 1st Qu.: 3894 1st Qu.: 14.0 1st Qu.: 5.00
Median :2013 Median : 51166 Median : 91.0 Median :10.00
Mean :2013 Mean :165026 Mean : 188.8 Mean :14.38
3rd Qu.:2013 3rd Qu.:208182 3rd Qu.: 228.0 3rd Qu.:29.00
Max. :2013 Max. :937431 Max. :1182.0 Max. :33.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2014 Min. : 54 Min. : 1.0 Min. : 1.00
1st Qu.:2014 1st Qu.: 2258 1st Qu.: 6.0 1st Qu.: 3.50
Median :2014 Median : 36467 Median : 92.5 Median :11.50
Mean :2014 Mean :109261 Mean :148.1 Mean :14.67
3rd Qu.:2014 3rd Qu.:173057 3rd Qu.:221.0 3rd Qu.:27.00
Max. :2014 Max. :377811 Max. :617.0 Max. :35.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2015 Min. : 460 Min. : 2.0 Min. : 1.00
1st Qu.:2015 1st Qu.: 7809 1st Qu.: 15.0 1st Qu.: 4.00
Median :2015 Median : 16874 Median : 61.0 Median : 9.00
Mean :2015 Mean : 83779 Mean : 191.1 Mean :15.38
3rd Qu.:2015 3rd Qu.: 95906 3rd Qu.: 217.0 3rd Qu.:25.00
Max. :2015 Max. :519569 Max. :1201.0 Max. :38.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2016 Min. : 1356 Min. : 5.0 Min. : 2.00
1st Qu.:2016 1st Qu.: 8771 1st Qu.: 28.5 1st Qu.: 6.25
Median :2016 Median : 22827 Median : 50.5 Median : 8.50
Mean :2016 Mean : 84134 Mean :146.9 Mean :15.00
3rd Qu.:2016 3rd Qu.: 44986 3rd Qu.: 82.0 3rd Qu.:20.00
Max. :2016 Max. :705181 Max. :674.0 Max. :50.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2017 Min. : 54 Min. : 1.0 Min. : 1.00
1st Qu.:2017 1st Qu.: 10522 1st Qu.: 13.0 1st Qu.: 5.00
Median :2017 Median : 40547 Median : 47.0 Median : 7.00
Mean :2017 Mean : 157054 Mean :180.3 Mean :10.53
3rd Qu.:2017 3rd Qu.: 88216 3rd Qu.:135.0 3rd Qu.:12.00
Max. :2017 Max. :1064228 Max. :946.0 Max. :34.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2018 Min. : 410.5 Min. : 5.0 Min. : 3.00
1st Qu.:2018 1st Qu.: 13413.3 1st Qu.: 12.0 1st Qu.: 5.00
Median :2018 Median : 45809.4 Median : 25.0 Median : 8.00
Mean :2018 Mean :152330.9 Mean :116.4 Mean :14.47
3rd Qu.:2018 3rd Qu.:156092.9 3rd Qu.:140.5 3rd Qu.:20.00
Max. :2018 Max. :990439.4 Max. :542.0 Max. :44.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2019 Min. : 6639 Min. : 8.0 Min. : 4.00
1st Qu.:2019 1st Qu.: 38410 1st Qu.: 49.0 1st Qu.: 9.00
Median :2019 Median : 90963 Median : 70.5 Median :16.00
Mean :2019 Mean :224450 Mean :221.6 Mean :20.86
3rd Qu.:2019 3rd Qu.:233874 3rd Qu.:323.5 3rd Qu.:33.50
Max. :2019 Max. :975922 Max. :914.0 Max. :45.00
Table 5. Statistical descriptors for bogue in Alboran Sea GSA-01
year bogue_catches bogue_fishingdays bogue_boats
Min. :2009 Min. : 72.0 Min. : 1.00 Min. : 1.000
1st Qu.:2009 1st Qu.: 276.1 1st Qu.: 2.00 1st Qu.: 1.000
Median :2009 Median : 2475.0 Median : 7.00 Median : 2.000
Mean :2009 Mean : 6073.1 Mean : 31.23 Mean : 4.308
3rd Qu.:2009 3rd Qu.: 5461.5 3rd Qu.: 43.00 3rd Qu.: 5.000
Max. :2009 Max. :34845.0 Max. :196.00 Max. :18.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2010 Min. : 23.2 Min. : 1.00 Min. : 1.000
1st Qu.:2010 1st Qu.: 616.8 1st Qu.: 3.25 1st Qu.: 2.000
Median :2010 Median : 3030.8 Median : 13.50 Median : 4.000
Mean :2010 Mean : 7136.6 Mean : 31.36 Mean : 5.071
3rd Qu.:2010 3rd Qu.: 5680.0 3rd Qu.: 38.50 3rd Qu.: 6.500
Max. :2010 Max. :42045.0 Max. :208.00 Max. :20.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2011 Min. : 203 Min. : 2.00 Min. : 1.000
1st Qu.:2011 1st Qu.: 2149 1st Qu.: 13.00 1st Qu.: 3.500
Median :2011 Median : 4612 Median : 45.00 Median : 5.000
Mean :2011 Mean : 16590 Mean : 51.73 Mean : 6.727
3rd Qu.:2011 3rd Qu.: 6472 3rd Qu.: 47.50 3rd Qu.: 8.500
Max. :2011 Max. :120345 Max. :253.00 Max. :20.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2012 Min. : 56 Min. : 1.00 Min. : 1.00
1st Qu.:2012 1st Qu.: 1512 1st Qu.: 11.25 1st Qu.: 3.50
Median :2012 Median : 4946 Median : 27.00 Median : 4.50
Mean :2012 Mean : 8895 Mean : 42.08 Mean : 6.25
3rd Qu.:2012 3rd Qu.: 7817 3rd Qu.: 43.75 3rd Qu.: 7.75
Max. :2012 Max. :42720 Max. :225.00 Max. :20.00
year bogue_catches bogue_fishingdays bogue_boats
Min. :2013 Min. : 260 Min. : 1.00 Min. : 1.000
1st Qu.:2013 1st Qu.: 627 1st Qu.: 4.00 1st Qu.: 2.000
Median :2013 Median : 1440 Median : 15.00 Median : 4.000
Mean :2013 Mean :11149 Mean : 43.54 Mean : 4.154
3rd Qu.:2013 3rd Qu.: 4785 3rd Qu.: 19.00 3rd Qu.: 4.000
Max. :2013 Max. :56173 Max. :362.00 Max. :18.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2014 Min. : 11.8 Min. : 1.00 Min. : 1.000
1st Qu.:2014 1st Qu.: 552.5 1st Qu.: 5.00 1st Qu.: 1.000
Median :2014 Median : 1813.4 Median : 13.00 Median : 3.000
Mean :2014 Mean : 14171.1 Mean : 66.23 Mean : 4.769
3rd Qu.:2014 3rd Qu.: 12627.4 3rd Qu.: 29.00 3rd Qu.: 6.000
Max. :2014 Max. :109013.5 Max. :658.00 Max. :24.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2015 Min. : 12.0 Min. : 1.00 Min. : 1.000
1st Qu.:2015 1st Qu.: 542.8 1st Qu.: 3.50 1st Qu.: 2.000
Median :2015 Median : 3300.0 Median : 23.00 Median : 3.000
Mean :2015 Mean :13265.3 Mean : 59.27 Mean : 4.733
3rd Qu.:2015 3rd Qu.: 8712.1 3rd Qu.: 32.50 3rd Qu.: 6.000
Max. :2015 Max. :90552.8 Max. :616.00 Max. :21.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2016 Min. : 93.31 Min. : 1.00 Min. : 1.000
1st Qu.:2016 1st Qu.: 1227.88 1st Qu.: 7.00 1st Qu.: 1.000
Median :2016 Median : 2537.78 Median : 20.50 Median : 3.000
Mean :2016 Mean :11408.74 Mean : 53.58 Mean : 4.833
3rd Qu.:2016 3rd Qu.: 8439.06 3rd Qu.: 34.00 3rd Qu.: 6.000
Max. :2016 Max. :75367.49 Max. :417.00 Max. :17.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2017 Min. : 56.7 Min. : 1.00 Min. : 1.00
1st Qu.:2017 1st Qu.: 1179.6 1st Qu.: 6.50 1st Qu.: 1.25
Median :2017 Median : 2519.9 Median : 13.00 Median : 3.00
Mean :2017 Mean : 9093.1 Mean : 55.60 Mean : 4.30
3rd Qu.:2017 3rd Qu.: 3144.0 3rd Qu.: 15.25 3rd Qu.: 3.00
Max. :2017 Max. :71735.7 Max. :446.00 Max. :21.00
year bogue_catches bogue_fishingdays bogue_boats
Min. :2018 Min. : 174.0 Min. : 2.00 Min. : 1.00
1st Qu.:2018 1st Qu.: 804.9 1st Qu.: 5.50 1st Qu.: 1.25
Median :2018 Median : 2356.4 Median : 10.50 Median : 2.00
Mean :2018 Mean : 5113.1 Mean : 25.40 Mean : 3.00
3rd Qu.:2018 3rd Qu.: 5254.3 3rd Qu.: 16.25 3rd Qu.: 2.75
Max. :2018 Max. :28802.8 Max. :167.00 Max. :11.00
year bogue_catches bogue_fishingdays bogue_boats
Min. :2019 Min. : 40.11 Min. : 1.00 Min. : 1.000
1st Qu.:2019 1st Qu.: 700.25 1st Qu.: 2.25 1st Qu.: 1.250
Median :2019 Median : 1487.38 Median : 8.00 Median : 2.500
Mean :2019 Mean : 10598.15 Mean : 21.64 Mean : 3.643
3rd Qu.:2019 3rd Qu.: 4051.79 3rd Qu.: 13.50 3rd Qu.: 3.750
Max. :2019 Max. :100437.60 Max. :156.00 Max. :17.000
Table 6. Statistical descriptors for sardine in Alboran Sea GSA-01
year sardina_catches sardina_fishingdays sardina_boats
Min. :2009 Min. : 854.9 Min. : 4.0 Min. : 2.0
1st Qu.:2009 1st Qu.: 5979.5 1st Qu.: 11.0 1st Qu.: 5.0
Median :2009 Median : 239612.1 Median : 222.0 Median :14.0
Mean :2009 Mean : 398141.7 Mean : 315.3 Mean :14.4
3rd Qu.:2009 3rd Qu.: 623331.0 3rd Qu.: 449.0 3rd Qu.:17.5
Max. :2009 Max. :1867912.5 Max. :1218.0 Max. :31.0
year sardina_catches sardina_fishingdays sardina_boats
Min. :2010 Min. : 1333 Min. : 3.0 Min. : 1.00
1st Qu.:2010 1st Qu.: 4308 1st Qu.: 8.0 1st Qu.: 4.50
Median :2010 Median : 126748 Median : 219.0 Median :10.00
Mean :2010 Mean : 488502 Mean : 405.6 Mean :16.07
3rd Qu.:2010 3rd Qu.: 650707 3rd Qu.: 668.0 3rd Qu.:28.00
Max. :2010 Max. :2405825 Max. :1951.0 Max. :41.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2011 Min. : 5.7 Min. : 1.0 Min. : 1.00
1st Qu.:2011 1st Qu.: 7274.0 1st Qu.: 12.0 1st Qu.: 3.50
Median :2011 Median : 148485.5 Median : 216.0 Median : 9.50
Mean :2011 Mean : 393331.1 Mean : 357.8 Mean :13.25
3rd Qu.:2011 3rd Qu.: 545347.6 3rd Qu.: 529.5 3rd Qu.:24.50
Max. :2011 Max. :1946618.0 Max. :1783.0 Max. :32.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2012 Min. : 212 Min. : 1.0 Min. : 1.00
1st Qu.:2012 1st Qu.: 4587 1st Qu.: 14.0 1st Qu.: 4.25
Median :2012 Median : 191812 Median : 285.0 Median :13.50
Mean :2012 Mean : 388354 Mean : 375.5 Mean :13.31
3rd Qu.:2012 3rd Qu.: 567473 3rd Qu.: 526.2 3rd Qu.:19.75
Max. :2012 Max. :1572348 Max. :1355.0 Max. :32.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2013 Min. : 85.8 Min. : 1.0 Min. : 1.0
1st Qu.:2013 1st Qu.: 11272.7 1st Qu.: 21.0 1st Qu.: 4.0
Median :2013 Median : 149807.2 Median : 234.0 Median :11.0
Mean :2013 Mean : 332233.1 Mean : 361.7 Mean :14.6
3rd Qu.:2013 3rd Qu.: 471691.7 3rd Qu.: 581.5 3rd Qu.:25.5
Max. :2013 Max. :1601099.0 Max. :1618.0 Max. :35.0
year sardina_catches sardina_fishingdays sardina_boats
Min. :2014 Min. : 746 Min. : 3.00 Min. : 2.00
1st Qu.:2014 1st Qu.: 42579 1st Qu.: 88.25 1st Qu.: 4.50
Median :2014 Median : 226258 Median : 330.50 Median :12.00
Mean :2014 Mean : 369571 Mean : 413.07 Mean :16.29
3rd Qu.:2014 3rd Qu.: 570593 3rd Qu.: 445.25 3rd Qu.:30.25
Max. :2014 Max. :1780224 Max. :1817.00 Max. :36.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2015 Min. : 7 Min. : 1.0 Min. : 1.00
1st Qu.:2015 1st Qu.: 36540 1st Qu.: 124.0 1st Qu.: 6.00
Median :2015 Median : 196972 Median : 326.0 Median :12.00
Mean :2015 Mean : 403719 Mean : 434.4 Mean :18.15
3rd Qu.:2015 3rd Qu.: 477578 3rd Qu.: 471.0 3rd Qu.:23.00
Max. :2015 Max. :2610666 Max. :2139.0 Max. :44.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2016 Min. : 480 Min. : 1.0 Min. : 1.00
1st Qu.:2016 1st Qu.: 80840 1st Qu.: 118.0 1st Qu.: 6.00
Median :2016 Median : 160617 Median : 310.0 Median :12.00
Mean :2016 Mean : 320830 Mean : 376.6 Mean :17.62
3rd Qu.:2016 3rd Qu.: 394350 3rd Qu.: 410.0 3rd Qu.:33.00
Max. :2016 Max. :1686107 Max. :1702.0 Max. :46.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2017 Min. : 24.2 Min. : 1.0 Min. : 1.00
1st Qu.:2017 1st Qu.: 20771.7 1st Qu.: 71.0 1st Qu.: 3.75
Median :2017 Median : 99643.6 Median : 182.0 Median : 7.50
Mean :2017 Mean : 261041.9 Mean : 315.8 Mean :10.14
3rd Qu.:2017 3rd Qu.: 313774.0 3rd Qu.: 433.0 3rd Qu.:11.50
Max. :2017 Max. :1182240.2 Max. :1243.0 Max. :32.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2018 Min. : 144 Min. : 2.0 Min. : 2.0
1st Qu.:2018 1st Qu.: 9505 1st Qu.: 40.0 1st Qu.: 5.0
Median :2018 Median : 87206 Median : 109.0 Median :10.0
Mean :2018 Mean : 220122 Mean : 231.5 Mean :14.2
3rd Qu.:2018 3rd Qu.: 307759 3rd Qu.: 374.0 3rd Qu.:21.0
Max. :2018 Max. :1176803 Max. :1060.0 Max. :39.0
year sardina_catches sardina_fishingdays sardina_boats
Min. :2019 Min. : 635.4 Min. : 3.0 Min. : 1.00
1st Qu.:2019 1st Qu.: 14641.6 1st Qu.: 34.0 1st Qu.: 4.50
Median :2019 Median : 41220.0 Median : 99.0 Median : 9.00
Mean :2019 Mean :160834.0 Mean : 224.7 Mean :13.07
3rd Qu.:2019 3rd Qu.:185295.1 3rd Qu.: 382.5 3rd Qu.:22.00
Max. :2019 Max. :912205.6 Max. :1034.0 Max. :33.00
Table 7. Statistical descriptors for sardinella in Alboran Sea GSA-01
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2009 Min. : 1162 Min. : 3.00 Min. : 3.000
1st Qu.:2009 1st Qu.: 17473 1st Qu.: 9.75 1st Qu.: 4.750
Median :2009 Median : 91825 Median : 48.50 Median : 7.500
Mean :2009 Mean :124007 Mean : 66.58 Mean : 9.833
3rd Qu.:2009 3rd Qu.:191462 3rd Qu.: 79.75 3rd Qu.:12.250
Max. :2009 Max. :368412 Max. :277.00 Max. :26.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2010 Min. : 616 Min. : 2.00 Min. : 2.000
1st Qu.:2010 1st Qu.: 8355 1st Qu.: 8.00 1st Qu.: 3.000
Median :2010 Median : 37773 Median : 21.00 Median : 6.000
Mean :2010 Mean : 92111 Mean : 48.08 Mean : 6.846
3rd Qu.:2010 3rd Qu.:134580 3rd Qu.: 55.00 3rd Qu.: 8.000
Max. :2010 Max. :332923 Max. :226.00 Max. :21.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2011 Min. : 8110 Min. : 6.0 Min. : 2.000
1st Qu.:2011 1st Qu.: 24361 1st Qu.: 10.0 1st Qu.: 3.000
Median :2011 Median : 84000 Median : 21.0 Median : 5.000
Mean :2011 Mean :224791 Mean :125.8 Mean : 8.818
3rd Qu.:2011 3rd Qu.:393650 3rd Qu.:133.5 3rd Qu.:10.000
Max. :2011 Max. :722595 Max. :601.0 Max. :31.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2012 Min. : 210 Min. : 1 Min. : 1.000
1st Qu.:2012 1st Qu.: 10514 1st Qu.: 10 1st Qu.: 2.000
Median :2012 Median : 58845 Median : 22 Median : 6.000
Mean :2012 Mean :177669 Mean :105 Mean : 8.077
3rd Qu.:2012 3rd Qu.:254688 3rd Qu.: 90 3rd Qu.: 9.000
Max. :2012 Max. :818575 Max. :569 Max. :30.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2013 Min. : 28 Min. : 1.00 Min. : 1.000
1st Qu.:2013 1st Qu.: 5881 1st Qu.: 5.25 1st Qu.: 1.000
Median :2013 Median : 18514 Median : 15.50 Median : 5.000
Mean :2013 Mean : 84670 Mean : 55.79 Mean : 6.857
3rd Qu.:2013 3rd Qu.: 66110 3rd Qu.: 41.75 3rd Qu.: 7.250
Max. :2013 Max. :483040 Max. :275.00 Max. :25.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2014 Min. : 3577 Min. : 7.00 Min. : 1.00
1st Qu.:2014 1st Qu.: 17277 1st Qu.: 19.25 1st Qu.: 3.25
Median :2014 Median : 43538 Median : 44.00 Median : 9.00
Mean :2014 Mean :106329 Mean :102.57 Mean :11.57
3rd Qu.:2014 3rd Qu.:176469 3rd Qu.:123.75 3rd Qu.:19.00
Max. :2014 Max. :327348 Max. :434.00 Max. :28.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2015 Min. : 573.5 Min. : 2.0 Min. : 1.000
1st Qu.:2015 1st Qu.: 12964.8 1st Qu.: 23.5 1st Qu.: 2.500
Median :2015 Median : 19561.1 Median : 55.0 Median : 6.000
Mean :2015 Mean :103468.3 Mean :103.7 Mean : 8.533
3rd Qu.:2015 3rd Qu.: 72957.8 3rd Qu.: 71.0 3rd Qu.:12.000
Max. :2015 Max. :631722.5 Max. :601.0 Max. :22.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2016 Min. : 13.7 Min. : 1.00 Min. : 1.000
1st Qu.:2016 1st Qu.: 9242.6 1st Qu.: 10.25 1st Qu.: 2.250
Median :2016 Median : 37764.7 Median : 18.50 Median : 4.000
Mean :2016 Mean : 70562.6 Mean : 58.00 Mean : 7.571
3rd Qu.:2016 3rd Qu.: 71901.6 3rd Qu.: 53.75 3rd Qu.: 9.000
Max. :2016 Max. :419806.8 Max. :271.00 Max. :30.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2017 Min. : 11.8 Min. : 1.0 Min. : 1.000
1st Qu.:2017 1st Qu.: 12883.5 1st Qu.: 7.0 1st Qu.: 2.000
Median :2017 Median : 39554.1 Median : 40.0 Median : 4.000
Mean :2017 Mean :189029.3 Mean :107.5 Mean : 7.385
3rd Qu.:2017 3rd Qu.:184884.3 3rd Qu.: 87.0 3rd Qu.: 9.000
Max. :2017 Max. :853968.0 Max. :571.0 Max. :27.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2018 Min. : 444 Min. : 2.00 Min. : 1.0
1st Qu.:2018 1st Qu.: 20936 1st Qu.: 11.00 1st Qu.: 2.0
Median :2018 Median : 75768 Median : 30.00 Median : 6.0
Mean :2018 Mean :129887 Mean : 69.07 Mean : 6.6
3rd Qu.:2018 3rd Qu.:180916 3rd Qu.: 87.00 3rd Qu.: 8.0
Max. :2018 Max. :406618 Max. :282.00 Max. :24.0
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2019 Min. : 142.6 Min. : 1.00 Min. : 1.000
1st Qu.:2019 1st Qu.: 22383.2 1st Qu.: 25.00 1st Qu.: 3.500
Median :2019 Median :125767.1 Median : 48.00 Median : 5.000
Mean :2019 Mean :222594.4 Mean : 94.13 Mean : 7.867
3rd Qu.:2019 3rd Qu.:315411.4 3rd Qu.: 99.50 3rd Qu.: 9.000
Max. :2019 Max. :796649.6 Max. :377.00 Max. :25.000
Table 8. Statistical descriptors for scomber in Alboran Sea GSA-01
year scomber_catches scomber_fishingdays scomber_boats
Min. :2009 Min. : 231 Min. : 7.0 Min. : 3.00
1st Qu.:2009 1st Qu.: 24964 1st Qu.: 58.5 1st Qu.: 7.50
Median :2009 Median : 53250 Median :131.0 Median : 9.00
Mean :2009 Mean : 199433 Mean :208.0 Mean :11.53
3rd Qu.:2009 3rd Qu.: 184273 3rd Qu.:290.5 3rd Qu.:12.50
Max. :2009 Max. :1245474 Max. :850.0 Max. :30.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2010 Min. : 2037 Min. : 13.00 Min. : 1.00
1st Qu.:2010 1st Qu.: 6692 1st Qu.: 37.25 1st Qu.: 6.50
Median :2010 Median : 37888 Median : 80.00 Median : 8.00
Mean :2010 Mean :112122 Mean :184.71 Mean :11.07
3rd Qu.:2010 3rd Qu.:180246 3rd Qu.:289.75 3rd Qu.:12.50
Max. :2010 Max. :483520 Max. :618.00 Max. :29.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2011 Min. : 1071 Min. : 26.0 Min. : 1.00
1st Qu.:2011 1st Qu.: 14918 1st Qu.: 52.5 1st Qu.: 7.25
Median :2011 Median : 44487 Median :104.5 Median : 8.50
Mean :2011 Mean : 174927 Mean :239.6 Mean :11.71
3rd Qu.:2011 3rd Qu.: 117844 3rd Qu.:328.5 3rd Qu.:16.25
Max. :2011 Max. :1092650 Max. :979.0 Max. :32.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2012 Min. : 33 Min. : 2.0 Min. : 1.00
1st Qu.:2012 1st Qu.: 8125 1st Qu.: 55.5 1st Qu.: 4.75
Median :2012 Median : 74912 Median : 120.0 Median : 9.00
Mean :2012 Mean : 268274 Mean : 274.5 Mean :11.44
3rd Qu.:2012 3rd Qu.: 333697 3rd Qu.: 389.5 3rd Qu.:13.00
Max. :2012 Max. :1770924 Max. :1404.0 Max. :34.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2013 Min. : 950 Min. : 8.0 Min. : 2.00
1st Qu.:2013 1st Qu.: 11428 1st Qu.: 36.5 1st Qu.: 5.00
Median :2013 Median : 70159 Median : 114.5 Median : 7.00
Mean :2013 Mean : 283947 Mean : 276.9 Mean :11.21
3rd Qu.:2013 3rd Qu.: 435368 3rd Qu.: 363.5 3rd Qu.:17.25
Max. :2013 Max. :1119215 Max. :1234.0 Max. :28.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2014 Min. : 2331 Min. : 7.00 Min. : 1.00
1st Qu.:2014 1st Qu.: 9190 1st Qu.: 41.75 1st Qu.: 4.00
Median :2014 Median : 23691 Median : 95.00 Median : 8.50
Mean :2014 Mean :129262 Mean : 235.29 Mean :11.43
3rd Qu.:2014 3rd Qu.:135519 3rd Qu.: 212.00 3rd Qu.:16.25
Max. :2014 Max. :675662 Max. :1231.00 Max. :29.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2015 Min. : 1372 Min. : 19.0 Min. : 1.00
1st Qu.:2015 1st Qu.: 5784 1st Qu.: 42.0 1st Qu.: 5.50
Median :2015 Median : 57866 Median : 98.0 Median : 9.50
Mean :2015 Mean : 72854 Mean :203.8 Mean :11.71
3rd Qu.:2015 3rd Qu.: 81288 3rd Qu.:293.8 3rd Qu.:14.25
Max. :2015 Max. :371442 Max. :937.0 Max. :34.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2016 Min. : 802.5 Min. : 8.0 Min. : 1.00
1st Qu.:2016 1st Qu.: 10232.3 1st Qu.: 27.0 1st Qu.: 4.25
Median :2016 Median : 21524.4 Median : 71.0 Median : 6.50
Mean :2016 Mean : 67709.5 Mean :135.0 Mean :11.14
3rd Qu.:2016 3rd Qu.: 50464.8 3rd Qu.:160.8 3rd Qu.:12.75
Max. :2016 Max. :596715.9 Max. :490.0 Max. :43.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2017 Min. : 92.9 Min. : 1.00 Min. : 1.000
1st Qu.:2017 1st Qu.: 6896.1 1st Qu.: 10.75 1st Qu.: 2.250
Median :2017 Median : 43469.6 Median : 110.50 Median : 4.500
Mean :2017 Mean :239667.4 Mean : 182.71 Mean : 8.143
3rd Qu.:2017 3rd Qu.:495571.7 3rd Qu.: 265.00 3rd Qu.: 8.750
Max. :2017 Max. :885099.4 Max. :1017.00 Max. :31.000
year scomber_catches scomber_fishingdays scomber_boats
Min. :2018 Min. : 20.7 Min. : 2.0 Min. : 2.000
1st Qu.:2018 1st Qu.: 2136.5 1st Qu.: 20.0 1st Qu.: 3.500
Median :2018 Median : 27898.8 Median : 56.0 Median : 5.000
Mean :2018 Mean : 127421.4 Mean :116.9 Mean : 8.867
3rd Qu.:2018 3rd Qu.: 90231.6 3rd Qu.:141.0 3rd Qu.:10.500
Max. :2018 Max. :1080521.1 Max. :666.0 Max. :33.000
year scomber_catches scomber_fishingdays scomber_boats
Min. :2019 Min. : 1126 Min. : 2.0 Min. : 1.000
1st Qu.:2019 1st Qu.: 5006 1st Qu.: 26.0 1st Qu.: 3.000
Median :2019 Median : 25100 Median : 70.0 Median : 4.000
Mean :2019 Mean : 158493 Mean :121.1 Mean : 8.667
3rd Qu.:2019 3rd Qu.: 197110 3rd Qu.:123.0 3rd Qu.:10.500
Max. :2019 Max. :1007416 Max. :614.0 Max. :32.000
Table 9. Statistical descriptors for trachurus in Alboran Sea GSA-01
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2009 Min. : 1805 Min. : 18.0 Min. : 2.00
1st Qu.:2009 1st Qu.: 27911 1st Qu.: 83.0 1st Qu.: 8.00
Median :2009 Median :102699 Median : 258.0 Median :12.00
Mean :2009 Mean :159370 Mean : 332.9 Mean :12.53
3rd Qu.:2009 3rd Qu.:243463 3rd Qu.: 406.0 3rd Qu.:14.50
Max. :2009 Max. :483555 Max. :1123.0 Max. :29.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2010 Min. : 33 Min. : 1.00 Min. : 1.00
1st Qu.:2010 1st Qu.: 21823 1st Qu.: 59.75 1st Qu.: 4.25
Median :2010 Median : 46866 Median : 194.50 Median :10.00
Mean :2010 Mean :183699 Mean : 289.31 Mean :11.62
3rd Qu.:2010 3rd Qu.:251654 3rd Qu.: 434.50 3rd Qu.:16.00
Max. :2010 Max. :856897 Max. :1019.00 Max. :28.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2011 Min. : 165 Min. : 4.00 Min. : 1.00
1st Qu.:2011 1st Qu.: 22294 1st Qu.: 77.25 1st Qu.: 4.50
Median :2011 Median : 59157 Median : 221.00 Median : 9.00
Mean :2011 Mean :152354 Mean : 328.38 Mean :11.62
3rd Qu.:2011 3rd Qu.:238998 3rd Qu.: 455.00 3rd Qu.:18.25
Max. :2011 Max. :529090 Max. :1253.00 Max. :33.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2012 Min. : 418 Min. : 3.0 Min. : 1.00
1st Qu.:2012 1st Qu.: 27505 1st Qu.: 93.5 1st Qu.: 5.00
Median :2012 Median : 231420 Median : 220.0 Median :11.00
Mean :2012 Mean : 248598 Mean : 357.5 Mean :12.13
3rd Qu.:2012 3rd Qu.: 281385 3rd Qu.: 493.5 3rd Qu.:13.00
Max. :2012 Max. :1409664 Max. :1593.0 Max. :33.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2013 Min. : 1868 Min. : 23.00 Min. : 3.00
1st Qu.:2013 1st Qu.: 16574 1st Qu.: 73.75 1st Qu.: 5.25
Median :2013 Median :100324 Median : 190.00 Median : 8.00
Mean :2013 Mean :185720 Mean : 318.71 Mean :11.36
3rd Qu.:2013 3rd Qu.:258716 3rd Qu.: 352.25 3rd Qu.:15.75
Max. :2013 Max. :889288 Max. :1471.00 Max. :30.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2014 Min. : 7869 Min. : 19.0 Min. : 1.00
1st Qu.:2014 1st Qu.: 23303 1st Qu.: 88.5 1st Qu.: 4.25
Median :2014 Median : 49063 Median : 184.5 Median : 9.00
Mean :2014 Mean :186406 Mean : 353.8 Mean :13.07
3rd Qu.:2014 3rd Qu.:175257 3rd Qu.: 301.5 3rd Qu.:19.25
Max. :2014 Max. :979824 Max. :1567.0 Max. :30.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2015 Min. : 7461 Min. : 34.00 Min. : 1.00
1st Qu.:2015 1st Qu.: 15608 1st Qu.: 97.75 1st Qu.: 6.25
Median :2015 Median : 76245 Median : 172.00 Median :11.00
Mean :2015 Mean :165488 Mean : 319.50 Mean :13.50
3rd Qu.:2015 3rd Qu.:239654 3rd Qu.: 385.25 3rd Qu.:21.75
Max. :2015 Max. :815430 Max. :1261.00 Max. :33.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2016 Min. : 5660 Min. : 2.0 Min. : 1.0
1st Qu.:2016 1st Qu.: 11810 1st Qu.: 73.5 1st Qu.: 4.5
Median :2016 Median : 62087 Median :179.0 Median : 9.0
Mean :2016 Mean :111721 Mean :223.2 Mean :13.0
3rd Qu.:2016 3rd Qu.:161800 3rd Qu.:240.5 3rd Qu.:21.5
Max. :2016 Max. :597213 Max. :826.0 Max. :38.0
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2017 Min. : 633.5 Min. : 2.0 Min. : 1.000
1st Qu.:2017 1st Qu.: 11153.4 1st Qu.: 65.0 1st Qu.: 3.750
Median :2017 Median : 46638.2 Median : 112.5 Median : 6.500
Mean :2017 Mean :137637.7 Mean : 327.0 Mean : 8.071
3rd Qu.:2017 3rd Qu.:194416.7 3rd Qu.: 371.0 3rd Qu.: 9.750
Max. :2017 Max. :736845.5 Max. :1476.0 Max. :25.000
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2018 Min. : 619.4 Min. : 12.0 Min. : 1.00
1st Qu.:2018 1st Qu.: 8444.2 1st Qu.: 59.5 1st Qu.: 4.50
Median :2018 Median : 47106.9 Median : 92.0 Median : 7.00
Mean :2018 Mean : 75430.5 Mean :186.1 Mean :10.47
3rd Qu.:2018 3rd Qu.: 80220.9 3rd Qu.:210.5 3rd Qu.:16.00
Max. :2018 Max. :445551.0 Max. :699.0 Max. :26.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2019 Min. : 1292 Min. : 10.0 Min. : 1.0
1st Qu.:2019 1st Qu.: 14646 1st Qu.: 63.0 1st Qu.: 4.5
Median :2019 Median : 41655 Median :108.0 Median : 7.0
Mean :2019 Mean : 65761 Mean :177.7 Mean : 9.8
3rd Qu.:2019 3rd Qu.: 79892 3rd Qu.:217.5 3rd Qu.:13.5
Max. :2019 Max. :340542 Max. :774.0 Max. :33.0

2.1.2 NORTHERN SPAIN GSA-06

The function summary() offers the principal statistical descriptors for the variables of each species and year.The function ggpairs() from ‘GGally’ was used to visialuzar the relationship between variables.

In this analysis, NA data are excluded.

Table 10. Statistical descriptors for anchovy in Northen Spain GSA-06
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2009 Min. : 130 Min. : 1.0 Min. : 1.00
1st Qu.:2009 1st Qu.: 267598 1st Qu.: 226.0 1st Qu.:14.75
Median :2009 Median : 364751 Median : 359.0 Median :31.00
Mean :2009 Mean : 593706 Mean : 489.3 Mean :30.90
3rd Qu.:2009 3rd Qu.: 627201 3rd Qu.: 476.0 3rd Qu.:45.75
Max. :2009 Max. :1706410 Max. :1638.0 Max. :68.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2010 Min. : 480 Min. : 1.0 Min. : 1.00
1st Qu.:2010 1st Qu.: 312870 1st Qu.: 339.0 1st Qu.:23.50
Median :2010 Median : 437580 Median : 406.0 Median :31.00
Mean :2010 Mean : 520410 Mean : 520.8 Mean :31.68
3rd Qu.:2010 3rd Qu.: 652430 3rd Qu.: 654.0 3rd Qu.:44.00
Max. :2010 Max. :1346961 Max. :1666.0 Max. :60.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2011 Min. : 10 Min. : 1.0 Min. : 1.00
1st Qu.:2011 1st Qu.: 243367 1st Qu.: 305.0 1st Qu.:19.00
Median :2011 Median : 400659 Median : 466.0 Median :29.00
Mean :2011 Mean : 498151 Mean : 524.6 Mean :26.79
3rd Qu.:2011 3rd Qu.: 692946 3rd Qu.: 607.0 3rd Qu.:36.00
Max. :2011 Max. :1255674 Max. :1431.0 Max. :53.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2012 Min. : 448 Min. : 7.0 Min. : 2.00
1st Qu.:2012 1st Qu.: 386278 1st Qu.: 377.5 1st Qu.:15.50
Median :2012 Median : 510160 Median : 566.0 Median :26.00
Mean :2012 Mean : 601784 Mean : 634.6 Mean :26.21
3rd Qu.:2012 3rd Qu.: 730682 3rd Qu.: 775.0 3rd Qu.:37.00
Max. :2012 Max. :1705575 Max. :1928.0 Max. :56.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2013 Min. : 289015 Min. : 227.0 Min. : 5.00
1st Qu.:2013 1st Qu.: 520193 1st Qu.: 419.0 1st Qu.:20.50
Median :2013 Median : 739787 Median : 556.0 Median :25.00
Mean :2013 Mean : 902571 Mean : 685.9 Mean :27.32
3rd Qu.:2013 3rd Qu.: 947000 3rd Qu.: 756.5 3rd Qu.:33.50
Max. :2013 Max. :2278143 Max. :1597.0 Max. :63.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2014 Min. : 3500 Min. : 4.0 Min. : 1.00
1st Qu.:2014 1st Qu.: 411820 1st Qu.: 358.2 1st Qu.:20.00
Median :2014 Median : 594793 Median : 557.0 Median :26.00
Mean :2014 Mean : 849734 Mean : 738.6 Mean :28.45
3rd Qu.:2014 3rd Qu.:1154522 3rd Qu.:1041.0 3rd Qu.:36.75
Max. :2014 Max. :2548460 Max. :2094.0 Max. :60.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2015 Min. : 28495 Min. : 33.0 Min. : 6.0
1st Qu.:2015 1st Qu.: 329205 1st Qu.: 332.0 1st Qu.:15.5
Median :2015 Median : 665518 Median : 619.0 Median :27.0
Mean :2015 Mean : 874889 Mean : 745.1 Mean :25.0
3rd Qu.:2015 3rd Qu.:1130412 3rd Qu.: 971.5 3rd Qu.:32.5
Max. :2015 Max. :2721191 Max. :2188.0 Max. :41.0
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2016 Min. : 57792 Min. : 66.0 Min. : 3.00
1st Qu.:2016 1st Qu.: 272259 1st Qu.: 260.2 1st Qu.:15.75
Median :2016 Median : 789237 Median : 634.5 Median :27.50
Mean :2016 Mean : 875087 Mean : 753.7 Mean :28.45
3rd Qu.:2016 3rd Qu.:1261575 3rd Qu.: 966.2 3rd Qu.:33.00
Max. :2016 Max. :2393284 Max. :1865.0 Max. :63.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2017 Min. : 7307 Min. : 10.0 Min. : 2.00
1st Qu.:2017 1st Qu.: 251158 1st Qu.: 216.0 1st Qu.:12.00
Median :2017 Median : 711778 Median : 491.0 Median :19.00
Mean :2017 Mean : 868663 Mean : 650.7 Mean :23.48
3rd Qu.:2017 3rd Qu.:1219661 3rd Qu.: 973.0 3rd Qu.:34.00
Max. :2017 Max. :2449573 Max. :1723.0 Max. :69.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2018 Min. : 1227 Min. : 1.0 Min. : 1.00
1st Qu.:2018 1st Qu.: 259537 1st Qu.: 106.5 1st Qu.: 8.50
Median :2018 Median : 716527 Median : 482.0 Median :20.50
Mean :2018 Mean : 888522 Mean : 525.4 Mean :22.92
3rd Qu.:2018 3rd Qu.:1368471 3rd Qu.: 815.0 3rd Qu.:26.50
Max. :2018 Max. :3104786 Max. :1755.0 Max. :68.00
year anchovy_catches anchovy_fishingdays anchovy_boats
Min. :2019 Min. : 149.2 Min. : 1.0 Min. : 1.00
1st Qu.:2019 1st Qu.: 48348.0 1st Qu.: 53.0 1st Qu.: 2.00
Median :2019 Median : 448717.0 Median : 322.0 Median :20.00
Mean :2019 Mean : 535587.0 Mean : 408.7 Mean :18.77
3rd Qu.:2019 3rd Qu.: 647707.0 3rd Qu.: 631.2 3rd Qu.:28.75
Max. :2019 Max. :2338944.4 Max. :1524.0 Max. :49.00
Table 11. Statistical descriptors for bogue in Northen Spain GSA-06
year bogue_catches bogue_fishingdays bogue_boats
Min. :2009 Min. : 29.64 Min. : 1.00 Min. : 1.000
1st Qu.:2009 1st Qu.: 390.00 1st Qu.: 4.50 1st Qu.: 2.500
Median :2009 Median : 2700.00 Median :13.00 Median : 5.000
Mean :2009 Mean : 5245.43 Mean :20.47 Mean : 6.333
3rd Qu.:2009 3rd Qu.: 6220.00 3rd Qu.:28.00 3rd Qu.: 9.000
Max. :2009 Max. :32662.50 Max. :69.00 Max. :17.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2010 Min. : 24.0 Min. : 1.0 Min. : 1.000
1st Qu.:2010 1st Qu.: 397.4 1st Qu.: 5.0 1st Qu.: 2.500
Median :2010 Median : 1230.0 Median :10.0 Median : 5.000
Mean :2010 Mean : 2652.9 Mean :18.6 Mean : 5.933
3rd Qu.:2010 3rd Qu.: 3864.7 3rd Qu.:18.5 3rd Qu.: 6.000
Max. :2010 Max. :13152.0 Max. :83.0 Max. :18.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2011 Min. : 22.5 Min. : 1.00 Min. : 1.000
1st Qu.:2011 1st Qu.: 324.3 1st Qu.: 3.50 1st Qu.: 2.000
Median :2011 Median : 1910.0 Median : 11.00 Median : 3.500
Mean :2011 Mean : 6562.0 Mean : 28.79 Mean : 6.571
3rd Qu.:2011 3rd Qu.: 6409.5 3rd Qu.: 33.75 3rd Qu.: 9.500
Max. :2011 Max. :33865.3 Max. :124.00 Max. :23.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2012 Min. : 30.0 Min. : 1.00 Min. : 1.000
1st Qu.:2012 1st Qu.: 250.0 1st Qu.: 2.00 1st Qu.: 2.000
Median :2012 Median : 759.7 Median : 5.00 Median : 3.000
Mean :2012 Mean :1684.6 Mean :12.18 Mean : 4.824
3rd Qu.:2012 3rd Qu.:1584.0 3rd Qu.:19.00 3rd Qu.: 7.000
Max. :2012 Max. :7032.0 Max. :52.00 Max. :16.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2013 Min. : 132.0 Min. : 1.00 Min. :1.000
1st Qu.:2013 1st Qu.: 437.5 1st Qu.: 4.25 1st Qu.:2.250
Median :2013 Median :1393.9 Median : 8.00 Median :3.500
Mean :2013 Mean :1833.1 Mean :13.36 Mean :3.857
3rd Qu.:2013 3rd Qu.:2883.8 3rd Qu.:17.75 3rd Qu.:5.500
Max. :2013 Max. :5285.0 Max. :41.00 Max. :8.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2014 Min. : 10.0 Min. : 1.00 Min. :1.000
1st Qu.:2014 1st Qu.: 140.9 1st Qu.: 2.00 1st Qu.:2.000
Median :2014 Median : 468.4 Median : 5.50 Median :2.500
Mean :2014 Mean :1228.2 Mean :12.43 Mean :3.071
3rd Qu.:2014 3rd Qu.:1555.5 3rd Qu.:11.75 3rd Qu.:4.000
Max. :2014 Max. :7277.5 Max. :84.00 Max. :6.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2015 Min. : 10.0 Min. : 1.000 Min. :1.000
1st Qu.:2015 1st Qu.: 40.0 1st Qu.: 2.000 1st Qu.:1.000
Median :2015 Median : 383.2 Median : 4.000 Median :2.000
Mean :2015 Mean : 866.3 Mean : 8.154 Mean :2.923
3rd Qu.:2015 3rd Qu.:1410.0 3rd Qu.:14.000 3rd Qu.:5.000
Max. :2015 Max. :4162.1 Max. :25.000 Max. :8.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2016 Min. : 18.5 Min. : 1.000 Min. :1.000
1st Qu.:2016 1st Qu.: 101.2 1st Qu.: 1.500 1st Qu.:1.000
Median :2016 Median : 212.3 Median : 6.000 Median :1.000
Mean :2016 Mean :1362.2 Mean : 8.818 Mean :2.364
3rd Qu.:2016 3rd Qu.:1289.8 3rd Qu.:12.500 3rd Qu.:4.000
Max. :2016 Max. :7733.1 Max. :35.000 Max. :5.000
year bogue_catches bogue_fishingdays bogue_boats
Min. :2017 Min. : 9.20 Min. : 1.0 Min. :1.0
1st Qu.:2017 1st Qu.: 27.14 1st Qu.: 2.0 1st Qu.:1.0
Median :2017 Median : 295.77 Median : 2.5 Median :2.0
Mean :2017 Mean : 511.44 Mean : 4.6 Mean :1.9
3rd Qu.:2017 3rd Qu.: 927.97 3rd Qu.: 8.5 3rd Qu.:2.0
Max. :2017 Max. :1512.40 Max. :11.0 Max. :4.0
year bogue_catches bogue_fishingdays bogue_boats
Min. :2018 Min. : 6.04 Min. : 1.00 Min. :1.0
1st Qu.:2018 1st Qu.:156.87 1st Qu.: 1.25 1st Qu.:1.0
Median :2018 Median :312.49 Median : 2.00 Median :1.5
Mean :2018 Mean :432.56 Mean : 4.40 Mean :2.0
3rd Qu.:2018 3rd Qu.:795.48 3rd Qu.: 5.75 3rd Qu.:2.0
Max. :2018 Max. :900.36 Max. :14.00 Max. :5.0
year bogue_catches bogue_fishingdays bogue_boats
Min. :2019 Min. : 75.95 Min. : 1.00 Min. :1.000
1st Qu.:2019 1st Qu.: 257.51 1st Qu.: 2.50 1st Qu.:1.000
Median :2019 Median :1456.13 Median : 5.50 Median :2.000
Mean :2019 Mean :1680.24 Mean : 6.25 Mean :2.167
3rd Qu.:2019 3rd Qu.:2214.97 3rd Qu.: 8.25 3rd Qu.:3.000
Max. :2019 Max. :5212.00 Max. :20.00 Max. :5.000
Table 12. Statistical descriptors for sardine in Northen Spain GSA-06
year sardina_catches sardina_fishingdays sardina_boats
Min. :2009 Min. : 89928 Min. : 197.0 Min. : 5.00
1st Qu.:2009 1st Qu.: 300269 1st Qu.: 355.0 1st Qu.:19.75
Median :2009 Median : 416050 Median : 554.5 Median :33.00
Mean :2009 Mean : 491464 Mean : 631.4 Mean :33.22
3rd Qu.:2009 3rd Qu.: 662750 3rd Qu.: 823.5 3rd Qu.:45.75
Max. :2009 Max. :1147929 Max. :1557.0 Max. :64.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2010 Min. : 131748 Min. : 184.0 Min. : 5.00
1st Qu.:2010 1st Qu.: 325690 1st Qu.: 333.0 1st Qu.:23.25
Median :2010 Median : 409300 Median : 481.5 Median :33.00
Mean :2010 Mean : 486347 Mean : 545.4 Mean :33.11
3rd Qu.:2010 3rd Qu.: 638265 3rd Qu.: 636.0 3rd Qu.:43.75
Max. :2010 Max. :1163896 Max. :1211.0 Max. :58.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2011 Min. : 33.5 Min. : 1.0 Min. : 1.0
1st Qu.:2011 1st Qu.: 286636.5 1st Qu.: 368.5 1st Qu.:14.5
Median :2011 Median : 587270.5 Median : 514.5 Median :28.5
Mean :2011 Mean : 606097.2 Mean : 578.1 Mean :26.3
3rd Qu.:2011 3rd Qu.: 844020.2 3rd Qu.: 791.8 3rd Qu.:38.5
Max. :2011 Max. :1639454.0 Max. :1794.0 Max. :54.0
year sardina_catches sardina_fishingdays sardina_boats
Min. :2012 Min. : 70675 Min. : 54.0 Min. : 3.00
1st Qu.:2012 1st Qu.: 203560 1st Qu.: 269.0 1st Qu.:14.50
Median :2012 Median : 470724 Median : 607.0 Median :26.00
Mean :2012 Mean : 483889 Mean : 593.3 Mean :25.42
3rd Qu.:2012 3rd Qu.: 722800 3rd Qu.: 683.5 3rd Qu.:37.00
Max. :2012 Max. :1087989 Max. :1700.0 Max. :55.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2013 Min. : 107822 Min. : 156.0 Min. : 5.00
1st Qu.:2013 1st Qu.: 251981 1st Qu.: 376.5 1st Qu.:20.00
Median :2013 Median : 519136 Median : 585.0 Median :23.00
Mean :2013 Mean : 512027 Mean : 596.4 Mean :25.95
3rd Qu.:2013 3rd Qu.: 652128 3rd Qu.: 738.0 3rd Qu.:34.00
Max. :2013 Max. :1260300 Max. :1270.0 Max. :48.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2014 Min. : 27283 Min. : 77.0 Min. : 5.00
1st Qu.:2014 1st Qu.: 232965 1st Qu.: 393.0 1st Qu.:19.50
Median :2014 Median : 431974 Median : 574.0 Median :23.00
Mean :2014 Mean : 511973 Mean : 687.6 Mean :28.32
3rd Qu.:2014 3rd Qu.: 878323 3rd Qu.: 882.5 3rd Qu.:36.00
Max. :2014 Max. :1102618 Max. :1474.0 Max. :55.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2015 Min. : 704.9 Min. : 8.0 Min. : 4.00
1st Qu.:2015 1st Qu.: 122972.0 1st Qu.: 219.5 1st Qu.:16.00
Median :2015 Median : 250288.2 Median : 486.0 Median :22.00
Mean :2015 Mean : 332965.1 Mean : 548.6 Mean :20.95
3rd Qu.:2015 3rd Qu.: 432629.1 3rd Qu.: 837.0 3rd Qu.:23.50
Max. :2015 Max. :1018414.7 Max. :1613.0 Max. :40.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2016 Min. : 24608 Min. : 56.0 Min. : 4.00
1st Qu.:2016 1st Qu.: 199096 1st Qu.: 224.5 1st Qu.:13.75
Median :2016 Median : 358009 Median : 538.0 Median :23.50
Mean :2016 Mean : 496710 Mean : 642.0 Mean :25.70
3rd Qu.:2016 3rd Qu.: 674912 3rd Qu.: 886.5 3rd Qu.:31.25
Max. :2016 Max. :1365948 Max. :1677.0 Max. :59.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2017 Min. : 1682 Min. : 4.0 Min. : 2.0
1st Qu.:2017 1st Qu.: 79556 1st Qu.: 121.0 1st Qu.: 8.0
Median :2017 Median : 245348 Median : 378.0 Median :17.0
Mean :2017 Mean : 341645 Mean : 487.8 Mean :21.1
3rd Qu.:2017 3rd Qu.: 537273 3rd Qu.: 705.0 3rd Qu.:30.0
Max. :2017 Max. :1028590 Max. :1332.0 Max. :67.0
year sardina_catches sardina_fishingdays sardina_boats
Min. :2018 Min. : 69.3 Min. : 1.0 Min. : 1.00
1st Qu.:2018 1st Qu.: 75098.2 1st Qu.: 108.0 1st Qu.: 8.00
Median :2018 Median : 376216.5 Median : 381.0 Median :19.00
Mean :2018 Mean : 362074.4 Mean : 413.3 Mean :22.09
3rd Qu.:2018 3rd Qu.: 526165.5 3rd Qu.: 614.5 3rd Qu.:25.00
Max. :2018 Max. :1051624.6 Max. :1291.0 Max. :63.00
year sardina_catches sardina_fishingdays sardina_boats
Min. :2019 Min. : 132.8 Min. : 1.0 Min. : 1.00
1st Qu.:2019 1st Qu.: 42413.9 1st Qu.: 114.8 1st Qu.: 6.50
Median :2019 Median : 154639.6 Median : 346.0 Median :21.00
Mean :2019 Mean : 279634.4 Mean : 391.3 Mean :19.71
3rd Qu.:2019 3rd Qu.: 364396.0 3rd Qu.: 626.8 3rd Qu.:29.25
Max. :2019 Max. :1254121.9 Max. :1336.0 Max. :46.00
Table 13. Statistical descriptors for sardinella in Northen Spain GSA-06
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2009 Min. : 1120 Min. : 1.00 Min. : 1.00
1st Qu.:2009 1st Qu.: 13033 1st Qu.: 13.75 1st Qu.: 7.00
Median :2009 Median : 33857 Median : 24.00 Median : 9.00
Mean :2009 Mean : 62337 Mean : 39.72 Mean :10.44
3rd Qu.:2009 3rd Qu.: 66258 3rd Qu.: 51.00 3rd Qu.:13.50
Max. :2009 Max. :302022 Max. :212.00 Max. :28.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2010 Min. : 6030 Min. : 6.00 Min. : 3.00
1st Qu.:2010 1st Qu.: 36060 1st Qu.: 28.00 1st Qu.: 9.00
Median :2010 Median :147930 Median : 62.00 Median :12.00
Mean :2010 Mean :160094 Mean : 74.71 Mean :13.76
3rd Qu.:2010 3rd Qu.:220188 3rd Qu.:103.00 3rd Qu.:18.00
Max. :2010 Max. :563724 Max. :273.00 Max. :30.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2011 Min. : 3540 Min. : 6.0 Min. : 2.00
1st Qu.:2011 1st Qu.: 34044 1st Qu.: 24.0 1st Qu.: 7.00
Median :2011 Median : 68672 Median : 45.0 Median :11.00
Mean :2011 Mean : 293994 Mean :114.8 Mean :15.29
3rd Qu.:2011 3rd Qu.: 297550 3rd Qu.:140.0 3rd Qu.:22.00
Max. :2011 Max. :1677468 Max. :585.0 Max. :44.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2012 Min. : 2940 Min. : 6.00 Min. : 3.00
1st Qu.:2012 1st Qu.: 21106 1st Qu.: 28.25 1st Qu.: 5.50
Median :2012 Median : 98550 Median : 55.50 Median : 8.00
Mean :2012 Mean : 209447 Mean : 95.83 Mean :13.61
3rd Qu.:2012 3rd Qu.: 244872 3rd Qu.:153.75 3rd Qu.:23.00
Max. :2012 Max. :1125660 Max. :436.00 Max. :32.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2013 Min. : 4190 Min. : 5.00 Min. : 2.000
1st Qu.:2013 1st Qu.: 11918 1st Qu.: 8.00 1st Qu.: 4.000
Median :2013 Median : 23052 Median : 14.00 Median : 5.000
Mean :2013 Mean : 202269 Mean : 58.71 Mean : 9.941
3rd Qu.:2013 3rd Qu.: 117250 3rd Qu.: 52.00 3rd Qu.:13.000
Max. :2013 Max. :1410288 Max. :331.00 Max. :39.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2014 Min. : 6 Min. : 1.00 Min. : 1.000
1st Qu.:2014 1st Qu.: 4798 1st Qu.: 5.00 1st Qu.: 3.000
Median :2014 Median : 13654 Median : 15.00 Median : 6.000
Mean :2014 Mean : 76643 Mean : 42.32 Mean : 7.895
3rd Qu.:2014 3rd Qu.: 20786 3rd Qu.: 40.00 3rd Qu.:11.000
Max. :2014 Max. :622589 Max. :252.00 Max. :27.000
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2015 Min. : 6690 Min. : 3.00 Min. : 2.00
1st Qu.:2015 1st Qu.: 36433 1st Qu.: 27.25 1st Qu.: 6.50
Median :2015 Median :108846 Median : 68.50 Median :14.50
Mean :2015 Mean :131569 Mean : 94.50 Mean :13.72
3rd Qu.:2015 3rd Qu.:187934 3rd Qu.: 98.50 3rd Qu.:18.75
Max. :2015 Max. :506239 Max. :498.00 Max. :34.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2016 Min. : 1844 Min. : 2.00 Min. : 2.0
1st Qu.:2016 1st Qu.: 9954 1st Qu.: 9.50 1st Qu.: 4.0
Median :2016 Median : 25181 Median : 27.00 Median : 9.0
Mean :2016 Mean : 57580 Mean : 40.11 Mean :11.0
3rd Qu.:2016 3rd Qu.: 72958 3rd Qu.: 54.50 3rd Qu.:15.5
Max. :2016 Max. :356083 Max. :135.00 Max. :37.0
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2017 Min. : 1331 Min. : 1.00 Min. : 1.0
1st Qu.:2017 1st Qu.: 16152 1st Qu.: 14.25 1st Qu.: 3.0
Median :2017 Median : 60703 Median : 44.00 Median : 8.5
Mean :2017 Mean :122241 Mean : 61.00 Mean :11.2
3rd Qu.:2017 3rd Qu.:141831 3rd Qu.: 95.75 3rd Qu.:16.0
Max. :2017 Max. :633536 Max. :180.00 Max. :36.0
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2018 Min. : 4012 Min. : 1.00 Min. : 1.00
1st Qu.:2018 1st Qu.: 23838 1st Qu.: 20.00 1st Qu.: 4.25
Median :2018 Median : 57775 Median : 42.00 Median : 8.00
Mean :2018 Mean : 98829 Mean : 54.44 Mean :10.50
3rd Qu.:2018 3rd Qu.:154121 3rd Qu.: 87.75 3rd Qu.:14.75
Max. :2018 Max. :324473 Max. :146.00 Max. :30.00
year sardinella_catches sardinella_fishingdays sardinella_boats
Min. :2019 Min. : 1184 Min. : 1.00 Min. : 1.00
1st Qu.:2019 1st Qu.: 22276 1st Qu.: 19.50 1st Qu.: 4.50
Median :2019 Median : 53040 Median : 41.00 Median :10.00
Mean :2019 Mean : 81359 Mean : 56.05 Mean :10.26
3rd Qu.:2019 3rd Qu.:110893 3rd Qu.: 74.00 3rd Qu.:13.00
Max. :2019 Max. :279393 Max. :281.00 Max. :32.00
Table 14. Statistical descriptors for scomber in Northen Spain GSA-06
year scomber_catches scomber_fishingdays scomber_boats
Min. :2009 Min. : 1544 Min. : 1.0 Min. : 1.00
1st Qu.:2009 1st Qu.: 11711 1st Qu.: 50.0 1st Qu.: 9.50
Median :2009 Median : 32407 Median :104.5 Median :19.50
Mean :2009 Mean : 65976 Mean :145.4 Mean :21.10
3rd Qu.:2009 3rd Qu.: 85041 3rd Qu.:213.5 3rd Qu.:35.25
Max. :2009 Max. :461809 Max. :506.0 Max. :47.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2010 Min. : 39.7 Min. : 2.0 Min. : 1.00
1st Qu.:2010 1st Qu.: 9026.5 1st Qu.: 80.0 1st Qu.:10.50
Median :2010 Median : 25347.9 Median :110.0 Median :20.00
Mean :2010 Mean : 64579.4 Mean :142.6 Mean :20.89
3rd Qu.:2010 3rd Qu.: 77290.5 3rd Qu.:203.5 3rd Qu.:28.50
Max. :2010 Max. :317122.6 Max. :466.0 Max. :45.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2011 Min. : 200 Min. : 4.00 Min. : 1.00
1st Qu.:2011 1st Qu.: 8570 1st Qu.: 35.75 1st Qu.: 7.50
Median :2011 Median : 15631 Median : 81.00 Median :15.50
Mean :2011 Mean : 37093 Mean :129.75 Mean :16.25
3rd Qu.:2011 3rd Qu.: 65096 3rd Qu.:199.50 3rd Qu.:23.50
Max. :2011 Max. :148492 Max. :421.00 Max. :41.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2012 Min. : 795.6 Min. : 17.00 Min. : 3.00
1st Qu.:2012 1st Qu.: 4674.7 1st Qu.: 28.00 1st Qu.: 7.50
Median :2012 Median : 11210.0 Median : 61.00 Median :15.00
Mean :2012 Mean : 27512.2 Mean : 92.05 Mean :13.79
3rd Qu.:2012 3rd Qu.: 37490.0 3rd Qu.:146.50 3rd Qu.:20.00
Max. :2012 Max. :110652.0 Max. :240.00 Max. :27.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2013 Min. : 1357 Min. : 25.0 Min. : 5.00
1st Qu.:2013 1st Qu.: 11518 1st Qu.: 62.0 1st Qu.: 8.50
Median :2013 Median : 21450 Median :109.0 Median :17.00
Mean :2013 Mean : 45263 Mean :123.1 Mean :16.68
3rd Qu.:2013 3rd Qu.: 77584 3rd Qu.:177.5 3rd Qu.:19.50
Max. :2013 Max. :167640 Max. :290.0 Max. :39.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2014 Min. : 692.3 Min. : 11.0 Min. : 5.0
1st Qu.:2014 1st Qu.: 15162.2 1st Qu.: 90.5 1st Qu.:11.5
Median :2014 Median : 20010.8 Median :128.0 Median :18.0
Mean :2014 Mean : 44989.6 Mean :159.6 Mean :18.0
3rd Qu.:2014 3rd Qu.: 64100.4 3rd Qu.:247.0 3rd Qu.:23.5
Max. :2014 Max. :185255.6 Max. :362.0 Max. :42.0
year scomber_catches scomber_fishingdays scomber_boats
Min. :2015 Min. : 96 Min. : 1.00 Min. : 1.00
1st Qu.:2015 1st Qu.: 7759 1st Qu.: 40.00 1st Qu.: 7.00
Median :2015 Median : 9368 Median : 74.00 Median :13.00
Mean :2015 Mean :26225 Mean : 97.79 Mean :12.89
3rd Qu.:2015 3rd Qu.:49294 3rd Qu.:126.00 3rd Qu.:16.50
Max. :2015 Max. :88663 Max. :295.00 Max. :34.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2016 Min. : 22.5 Min. : 2.00 Min. : 2.00
1st Qu.:2016 1st Qu.: 5686.8 1st Qu.: 33.25 1st Qu.: 5.50
Median :2016 Median : 11515.3 Median : 58.00 Median :13.00
Mean :2016 Mean : 28945.5 Mean : 82.45 Mean :13.55
3rd Qu.:2016 3rd Qu.: 25967.0 3rd Qu.:124.25 3rd Qu.:17.25
Max. :2016 Max. :197921.8 Max. :247.00 Max. :40.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2017 Min. : 197.8 Min. : 4.0 Min. : 2.00
1st Qu.:2017 1st Qu.: 6032.5 1st Qu.: 37.5 1st Qu.: 4.50
Median :2017 Median : 25165.0 Median :103.0 Median :13.00
Mean :2017 Mean : 65016.3 Mean :120.4 Mean :14.63
3rd Qu.:2017 3rd Qu.: 47578.6 3rd Qu.:172.0 3rd Qu.:19.00
Max. :2017 Max. :613979.8 Max. :396.0 Max. :46.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2018 Min. : 46 Min. : 1.00 Min. : 1.00
1st Qu.:2018 1st Qu.: 7297 1st Qu.: 35.00 1st Qu.: 5.00
Median :2018 Median : 12072 Median : 62.00 Median : 8.00
Mean :2018 Mean : 27814 Mean : 78.05 Mean :12.05
3rd Qu.:2018 3rd Qu.: 33046 3rd Qu.: 99.00 3rd Qu.:17.00
Max. :2018 Max. :107787 Max. :242.00 Max. :33.00
year scomber_catches scomber_fishingdays scomber_boats
Min. :2019 Min. : 542.1 Min. : 3.00 Min. : 2.00
1st Qu.:2019 1st Qu.: 5714.4 1st Qu.: 37.50 1st Qu.: 6.00
Median :2019 Median : 7817.3 Median : 53.00 Median :10.00
Mean :2019 Mean : 31679.5 Mean : 85.89 Mean :12.32
3rd Qu.:2019 3rd Qu.: 54340.7 3rd Qu.:122.00 3rd Qu.:17.50
Max. :2019 Max. :148802.9 Max. :300.00 Max. :33.00
Table 15. Statistical descriptors for trachurus in Northen Spain GSA-06
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2009 Min. : 7 Min. : 1.0 Min. : 1.00
1st Qu.:2009 1st Qu.: 17527 1st Qu.: 52.0 1st Qu.: 8.00
Median :2009 Median : 49997 Median :127.0 Median :17.00
Mean :2009 Mean : 76468 Mean :176.3 Mean :18.53
3rd Qu.:2009 3rd Qu.:104189 3rd Qu.:233.0 3rd Qu.:30.50
Max. :2009 Max. :348176 Max. :656.0 Max. :37.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2010 Min. : 10.5 Min. : 1.00 Min. : 1.00
1st Qu.:2010 1st Qu.: 8486.0 1st Qu.: 23.25 1st Qu.: 5.75
Median :2010 Median : 18850.0 Median : 60.50 Median :15.00
Mean :2010 Mean : 45745.3 Mean :120.85 Mean :15.95
3rd Qu.:2010 3rd Qu.: 55047.2 3rd Qu.:174.25 3rd Qu.:23.25
Max. :2010 Max. :243347.9 Max. :469.00 Max. :40.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2011 Min. : 370 Min. : 5.00 Min. : 1.00
1st Qu.:2011 1st Qu.: 3429 1st Qu.: 19.75 1st Qu.: 5.75
Median :2011 Median : 21533 Median : 73.00 Median : 9.50
Mean :2011 Mean : 55947 Mean :136.00 Mean :14.05
3rd Qu.:2011 3rd Qu.: 88679 3rd Qu.:158.50 3rd Qu.:21.25
Max. :2011 Max. :212749 Max. :486.00 Max. :39.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2012 Min. : 50 Min. : 3.00 Min. : 2.00
1st Qu.:2012 1st Qu.: 5164 1st Qu.: 27.50 1st Qu.: 5.50
Median :2012 Median : 13796 Median : 58.00 Median : 9.00
Mean :2012 Mean : 27802 Mean : 95.42 Mean :12.58
3rd Qu.:2012 3rd Qu.: 33946 3rd Qu.:124.00 3rd Qu.:18.00
Max. :2012 Max. :125175 Max. :424.00 Max. :30.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2013 Min. : 10 Min. : 1.0 Min. : 1.0
1st Qu.:2013 1st Qu.: 3944 1st Qu.: 26.5 1st Qu.: 6.0
Median :2013 Median :11250 Median : 54.0 Median : 9.5
Mean :2013 Mean :19566 Mean : 72.9 Mean :11.5
3rd Qu.:2013 3rd Qu.:31665 3rd Qu.:100.5 3rd Qu.:14.0
Max. :2013 Max. :59538 Max. :236.0 Max. :30.0
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2014 Min. : 12.5 Min. : 1.0 Min. : 1.00
1st Qu.:2014 1st Qu.: 2722.0 1st Qu.: 25.0 1st Qu.: 8.25
Median :2014 Median : 7738.3 Median : 57.0 Median :11.00
Mean :2014 Mean :18475.9 Mean : 85.5 Mean :13.20
3rd Qu.:2014 3rd Qu.:24074.5 3rd Qu.:116.8 3rd Qu.:18.00
Max. :2014 Max. :77891.8 Max. :307.0 Max. :36.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2015 Min. : 523.9 Min. : 12.00 Min. : 2.00
1st Qu.:2015 1st Qu.: 4564.7 1st Qu.: 29.25 1st Qu.: 6.00
Median :2015 Median : 8609.3 Median : 50.00 Median :10.00
Mean :2015 Mean :14836.9 Mean :101.94 Mean :12.61
3rd Qu.:2015 3rd Qu.:24506.8 3rd Qu.:142.25 3rd Qu.:15.75
Max. :2015 Max. :43575.2 Max. :367.00 Max. :36.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2016 Min. : 6 Min. : 1.00 Min. : 1.00
1st Qu.:2016 1st Qu.: 2249 1st Qu.: 22.00 1st Qu.: 7.00
Median :2016 Median : 8100 Median : 38.00 Median : 9.00
Mean :2016 Mean :12065 Mean : 77.16 Mean :12.42
3rd Qu.:2016 3rd Qu.:21070 3rd Qu.:108.00 3rd Qu.:14.50
Max. :2016 Max. :39796 Max. :289.00 Max. :44.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2017 Min. : 14 Min. : 1.00 Min. : 1.00
1st Qu.:2017 1st Qu.: 2047 1st Qu.: 19.00 1st Qu.: 3.50
Median :2017 Median : 3740 Median : 36.00 Median : 8.00
Mean :2017 Mean : 9372 Mean : 68.79 Mean :10.21
3rd Qu.:2017 3rd Qu.:10814 3rd Qu.: 68.50 3rd Qu.:11.00
Max. :2017 Max. :30502 Max. :314.00 Max. :37.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2018 Min. : 271.8 Min. : 2.00 Min. : 1.00
1st Qu.:2018 1st Qu.: 3932.1 1st Qu.: 28.00 1st Qu.: 4.50
Median :2018 Median : 7524.6 Median : 66.00 Median : 8.00
Mean :2018 Mean :12298.0 Mean : 83.21 Mean :11.95
3rd Qu.:2018 3rd Qu.:19275.6 3rd Qu.: 96.50 3rd Qu.:17.50
Max. :2018 Max. :33366.4 Max. :265.00 Max. :28.00
year trachurus_catches trachurus_fishingdays trachurus_boats
Min. :2019 Min. : 60 Min. : 1.00 Min. : 1.00
1st Qu.:2019 1st Qu.: 2345 1st Qu.: 16.00 1st Qu.: 3.00
Median :2019 Median : 5152 Median : 33.00 Median : 8.00
Mean :2019 Mean :17014 Mean : 63.67 Mean : 9.81
3rd Qu.:2019 3rd Qu.:21310 3rd Qu.:105.00 3rd Qu.:16.00
Max. :2019 Max. :85030 Max. :302.00 Max. :25.00

2.2 DATABASE ADAPTATION

Some harbours just have captures for some species in a few years. In this step, we check how many harbours have captures for less than 30% of the years. In this step is important to consider that we have a different number of the harbours in the different datasets due to the NA being excluded.

All the harbours that appear less than 4 times in each species are erased. In fish zone Alboran Sea GSA-01: 4 harbours in data_G1, 7 harbours for anchovy, 4 harbours for bogue, 5 harbours for sardine, 3 harbours for sardinella, 4 harbours for scomber and 3 harbours for trachurus.In fish zone Northern Spain GSA-06: 1 harbours in data_G6, 2 harbours for anchovy and 2 harbours for scomber.

Finally, a new datasets are calculated with the median of all numeric variables for each harbour.

3 CLUSTERING ANALYSIS

The first step for clustering analysis is the standardization of the data.

3.1 ALBORAN SEA GSA-01

3.1.1 Complete dataset

3.1.1.1 Hierarchical PCA clustering

Table 16. Cluster allocation: Complete Dataset Alboran Sea GSA-01
median(bogue_catches) median(bogue_fishingdays) median(bogue_boats) median(anchovy_catches) median(anchovy_fishingdays) median(anchovy_boats) median(sardina_catches) median(sardina_fishingdays) median(sardina_boats) median(sardinella_catches) median(sardinella_fishingdays) median(sardinella_boats) median(scomber_catches) median(scomber_fishingdays) median(scomber_boats) median(trachurus_catches) median(trachurus_fishingdays) median(trachurus_boats) clust
Adra -0.3054759 -0.1088424 0.3789324 -0.5722361 -0.4724703 -0.4188474 -0.2600806 -0.1795117 0.0821940 0.1440612 0.0046851 0.1306532 0.5901320 0.3741515 0.3046942 0.4153045 0.1185404 0.0626914 3
Algeciras -0.2017347 -0.3169235 -0.1473626 -0.5608942 -0.3842304 -0.6980790 -0.6439922 -0.5578903 -0.6336889 -0.6245826 -0.5374436 -0.4992817 -0.4383514 0.0884282 -0.4075779 1.2018907 1.1673627 -0.5250405 3
Almería 2.3800154 3.5405793 3.3261842 -0.3719501 0.0151711 0.6980790 0.3181002 0.8390168 1.1957896 1.6192911 2.4241853 1.9504651 3.2057424 3.1020785 1.8479503 2.8261434 2.8980724 1.5907944 6
Carboneras -0.4707206 -0.3489359 -0.2526216 -0.3162259 -0.4724703 -0.4188474 -0.7202775 -0.8208515 -0.9518591 -0.2942828 -0.3115567 -0.1493179 -0.3588851 -0.6415217 -0.4669339 -0.3960304 -0.7682190 -0.5250405 1
Cartagena -0.5711413 -0.3169235 -0.6736576 -0.6609328 -0.5839312 -0.9773107 -0.7199606 -0.8125224 -0.9518591 -0.6613173 -0.5173648 -0.8492456 -0.5820829 -0.5956391 -1.0011379 -0.8367577 -0.7315255 -1.1715457 1
Estepona 2.4372430 -0.0928362 0.1684144 -0.2339508 -0.3726199 -0.4188474 1.1836044 0.5498785 -0.3552900 -0.4000515 -0.4772071 -0.1493179 -0.2658412 -0.2723756 -0.2888659 0.0703163 -0.0297625 -0.2899478 3
Fuengirola -0.4567579 -0.4769858 -0.6736576 0.4500480 0.2473812 1.1634651 0.4252422 0.3440310 1.0367045 -0.6412199 -0.6077196 -0.5692745 -0.6053090 -0.7270301 -0.2888659 -0.7798712 -0.6397918 0.1802378 2
Garrucha -0.4536412 -0.1328518 -0.5683986 -0.6636100 -0.5862533 -1.1169265 -0.7177681 -0.8006237 -1.0314017 -0.5246185 -0.3215961 -0.7092600 -0.5418321 -0.5810401 -0.9417819 -0.5411007 -0.5939249 -0.9952261 1
Marbella 0.2112368 -0.4369702 -0.4631396 -0.5194461 -0.5212344 -0.6980790 0.4567593 0.0929685 -0.0768911 -0.5278505 -0.5725816 -0.4992817 -0.5916333 -0.6352650 -0.2295099 -0.7452859 -0.5235958 -0.2311746 1
Mazarrón -0.5424022 -0.4129609 -0.0421036 1.8012482 0.5074566 1.3961581 -0.3471421 0.0465636 1.3151034 2.7956964 2.3840276 2.5104073 0.6752734 0.6119066 2.1447303 0.3674689 0.0451534 1.8258872 5
Motril -0.2516039 0.0032012 -0.0421036 -0.6164733 -0.4956913 -0.9773107 -0.5390862 -0.4412830 -0.7927740 0.8098855 0.1251581 -0.4292890 0.5149456 0.3240978 -0.6450019 0.3977301 0.0115177 -0.7601333 3
Málaga -0.5870047 -0.4929921 -0.6736576 0.1962381 0.7814647 1.9080827 0.0117221 0.2440820 1.6730448 -0.6669806 -0.6077196 -0.5692745 -0.5965772 -0.6227515 0.0672701 -0.7088964 -0.3737640 1.2381553 2
Roquetas de Mar -0.4883182 -0.3489359 -0.4631396 -0.4281746 -0.4980134 -0.5584632 -0.7140329 -0.7839655 -0.9916304 -0.5230230 -0.5023056 -0.7092600 -0.5718563 -0.5935535 -0.8230699 -0.7857393 -0.7559879 -0.9952261 1
Vélez-Málaga -0.1638077 0.2432948 0.5894504 2.7897705 3.2707578 1.2565423 2.9477305 3.0212380 1.1957896 -0.2642583 -0.1961033 0.8305808 0.0442512 0.7495543 1.3731022 0.2802268 0.9043927 1.2381553 4
Águilas -0.5358869 -0.3009172 -0.4631396 -0.2934110 -0.4353167 -0.1396158 -0.6808185 -0.7411302 -0.7132315 -0.2407492 -0.2864581 -0.2893034 -0.4779761 -0.5810401 -0.6450019 -0.7653993 -0.7284677 -0.6425869 1

The most influential variables in the different clusters are analyzed below and the 8 most influential variables in the cluster analysis are represented.

## $`1`
##                                  v.test Mean in category  Overall mean
## median(sardina_fishingdays)   -2.037627       -0.6443541 -3.099373e-17
## median(anchovy_boats)         -2.060352       -0.6515404  9.251859e-18
## median(trachurus_catches)     -2.145243       -0.6783856 -2.220446e-17
## median(trachurus_fishingdays) -2.161797       -0.6836201 -4.186466e-17
## median(scomber_boats)         -2.164809       -0.6845726 -3.608225e-17
## median(trachurus_boats)       -2.403753       -0.7601333 -2.960595e-17
## median(sardina_boats)         -2.486010       -0.7861455 -3.700743e-17
##                               sd in category Overall sd    p.value
## median(sardina_fishingdays)       0.33074503  0.9660918 0.04158728
## median(anchovy_boats)             0.32907762  0.9660918 0.03936492
## median(trachurus_catches)         0.15664460  0.9660918 0.03193338
## median(trachurus_fishingdays)     0.09160907  0.9660918 0.03063385
## median(scomber_boats)             0.27128284  0.9660918 0.03040235
## median(trachurus_boats)           0.32369751  0.9660918 0.01622775
## median(sardina_boats)             0.33308028  0.9660918 0.01291842
## 
## $`2`
##                         v.test Mean in category  Overall mean sd in category
## median(anchovy_boats) 2.333008         1.535774  9.251859e-18      0.3723088
## median(sardina_boats) 2.058202         1.354875 -3.700743e-17      0.3181702
##                       Overall sd    p.value
## median(anchovy_boats)  0.9660918 0.01964772
## median(sardina_boats)  0.9660918 0.03957071
## 
## $`3`
## NULL
## 
## $`4`
##                               v.test Mean in category  Overall mean
## median(anchovy_fishingdays) 3.385556         3.270758  5.713023e-17
## median(sardina_fishingdays) 3.127278         3.021238 -3.099373e-17
## median(sardina_catches)     3.051191         2.947731  2.139492e-17
## median(anchovy_catches)     2.887687         2.789771 -1.850372e-17
##                             sd in category Overall sd      p.value
## median(anchovy_fishingdays)              0  0.9660918 0.0007103423
## median(sardina_fishingdays)              0  0.9660918 0.0017643276
## median(sardina_catches)                  0  0.9660918 0.0022793554
## median(anchovy_catches)                  0  0.9660918 0.0038808602
## 
## $`5`
##                                  v.test Mean in category  Overall mean
## median(sardinella_catches)     2.893821         2.795696 -5.551115e-18
## median(sardinella_boats)       2.598518         2.510407  3.145632e-17
## median(sardinella_fishingdays) 2.467703         2.384028  4.776272e-17
## median(scomber_boats)          2.220007         2.144730 -3.608225e-17
##                                sd in category Overall sd     p.value
## median(sardinella_catches)                  0  0.9660918 0.003805855
## median(sardinella_boats)                    0  0.9660918 0.009362703
## median(sardinella_fishingdays)              0  0.9660918 0.013598309
## median(scomber_boats)                       0  0.9660918 0.026418309
## 
## $`6`
##                                  v.test Mean in category  Overall mean
## median(bogue_fishingdays)      3.664848         3.540579 -3.414514e-17
## median(bogue_boats)            3.442928         3.326184 -2.590520e-17
## median(scomber_catches)        3.318259         3.205742 -5.643634e-17
## median(scomber_fishingdays)    3.210956         3.102079 -1.757853e-17
## median(trachurus_fishingdays)  2.999790         2.898072 -4.186466e-17
## median(trachurus_catches)      2.925336         2.826143 -2.220446e-17
## median(sardinella_fishingdays) 2.509270         2.424185  4.776272e-17
## median(bogue_catches)          2.463550         2.380015  1.110223e-17
## median(sardinella_boats)       2.018923         1.950465  3.145632e-17
##                                sd in category Overall sd      p.value
## median(bogue_fishingdays)                   0  0.9660918 0.0002474858
## median(bogue_boats)                         0  0.9660918 0.0005754531
## median(scomber_catches)                     0  0.9660918 0.0009058058
## median(scomber_fishingdays)                 0  0.9660918 0.0013229404
## median(trachurus_fishingdays)               0  0.9660918 0.0027016586
## median(trachurus_catches)                   0  0.9660918 0.0034408406
## median(sardinella_fishingdays)              0  0.9660918 0.0120980921
## median(bogue_catches)                       0  0.9660918 0.0137568717
## median(sardinella_boats)                    0  0.9660918 0.0434952009

3.1.1.2 Hierarchical K-mean clustering

3.1.2 Anchovy

3.1.2.1 Hierarchical PCA clustering

Table 17.Cluster allocation: Anchovy - Alboran Sea GSA-01
median(anchovy_catches) median(anchovy_fishingdays) median(anchovy_boats) clust
Adra -0.4651033 -0.4048463 -0.2770945 1
Algeciras -0.4100442 -0.3185750 -0.5694139 1
Almería -0.2513688 0.0984033 0.8921832 2
Carboneras -0.4367252 -0.4767391 -0.4719741 1
Cartagena -0.5632900 -0.5294605 -0.9591731 1
Estepona -0.1387950 -0.2850250 -0.2770945 1
Fuengirola -0.1061838 -0.0741394 0.5024240 1
Garrucha -0.5626125 -0.5222712 -1.0078930 1
La Atunara -0.5864010 -0.5438390 -1.0566129 1
Marbella -0.5001420 -0.4815320 -0.5694139 1
Mazarrón 1.6481338 0.4914172 1.5742619 2
Motril -0.5123108 -0.4288106 -0.8617333 1
Málaga 0.3549712 0.7981598 2.0614609 2
Roquetas de Mar -0.4858672 -0.4336034 -0.4719741 1
Vélez-Málaga 3.1226500 3.4581933 1.4768221 3
Águilas -0.1069110 -0.3473321 0.0152250 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(anchovy_fishingdays) -2.797940       -0.4038478 -1.387779e-17
## median(anchovy_catches)     -2.814228       -0.4061988  1.040834e-17
## median(anchovy_boats)       -3.466831       -0.5003940 -1.626303e-18
##                             sd in category Overall sd      p.value
## median(anchovy_fishingdays)      0.1276550  0.9682458 0.0051429728
## median(anchovy_catches)          0.1740573  0.9682458 0.0048894514
## median(anchovy_boats)            0.4368785  0.9682458 0.0005266321
## 
## $`2`
##                         v.test Mean in category  Overall mean sd in category
## median(anchovy_boats) 2.900181         1.509302 -1.626303e-18      0.4795605
##                       Overall sd     p.value
## median(anchovy_boats)  0.9682458 0.003729473
## 
## $`3`
##                               v.test Mean in category  Overall mean
## median(anchovy_fishingdays) 3.571607         3.458193 -1.387779e-17
## median(anchovy_catches)     3.225059         3.122650  1.040834e-17
##                             sd in category Overall sd     p.value
## median(anchovy_fishingdays)              0  0.9682458 0.000354798
## median(anchovy_catches)                  0  0.9682458 0.001259467

3.1.2.2 Hierarchical K-mean clustering

3.1.3 Bogue

3.1.3.1 Hierarchical PCA clustering

Table 18. Cluster allocation: bogue - Alboran Sea GSA-01
median(bogue_catches) median(bogue_fishingdays) median(bogue_boats) clust
Adra -0.2899312 -0.0939509 0.3948918 1
Algeciras -0.1139464 -0.2531897 -0.0207838 1
Almería 2.4595586 3.5366934 3.3046208 3
Carboneras -0.5688814 -0.4044665 -0.4364593 1
Cartagena -0.5434586 -0.1894942 -0.6442971 1
Estepona 2.3347375 -0.0621031 0.1870540 2
Fuengirola -0.4448185 -0.4602001 -0.6442971 1
Garrucha -0.5286541 -0.3885426 -0.6442971 1
Marbella 0.2390954 -0.4203904 -0.4364593 1
Mazarrón -0.5325038 -0.3965046 -0.0207838 1
Motril -0.2347753 0.0175163 -0.0207838 1
Málaga -0.5792175 -0.4761240 -0.6442971 1
Roquetas de Mar -0.5361324 -0.3805807 -0.5403782 1
Vélez-Málaga -0.1448869 0.2563744 0.6027296 1
Águilas -0.5161853 -0.2850374 -0.4364593 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                              v.test Mean in category Overall mean
## median(bogue_fishingdays) -2.639141       -0.2672762 1.249001e-17
## median(bogue_boats)       -2.652117       -0.2685904 7.470876e-17
## median(bogue_catches)     -3.641529       -0.3687920 3.238150e-17
##                           sd in category Overall sd      p.value
## median(bogue_fishingdays)      0.2074141  0.9660918 0.0083116464
## median(bogue_boats)            0.4025555  0.9660918 0.0079988732
## median(bogue_catches)          0.2372072  0.9660918 0.0002710233
## 
## $`2`
##                         v.test Mean in category Overall mean sd in category
## median(bogue_catches) 2.416683         2.334737  3.23815e-17              0
##                       Overall sd    p.value
## median(bogue_catches)  0.9660918 0.01566266
## 
## $`3`
##                             v.test Mean in category Overall mean sd in category
## median(bogue_fishingdays) 3.660825         3.536693 1.249001e-17              0
## median(bogue_boats)       3.420607         3.304621 7.470876e-17              0
## median(bogue_catches)     2.545885         2.459559 3.238150e-17              0
##                           Overall sd      p.value
## median(bogue_fishingdays)  0.9660918 0.0002514040
## median(bogue_boats)        0.9660918 0.0006248145
## median(bogue_catches)      0.9660918 0.0109001089

3.1.3.2 Hierarchical K-mean clustering

3.1.4 Sardine

3.1.4.1 Hierarchical PCA clustering

Table 19. Cluster allocation: sardine - Alboran Sea GSA-01
median(sardina_catches) median(sardina_fishingdays) median(sardina_boats) clust
Adra -0.1293767 -0.0991797 0.2033399 2
Algeciras -0.5818721 -0.4526258 -0.5474535 1
Almería 0.4024421 0.9369497 1.3712406 2
Carboneras -0.6544150 -0.7455502 -0.7977179 1
Cartagena -0.6568072 -0.7431294 -0.9228502 1
Estepona 1.2882550 0.6428148 -0.2554783 2
Fuengirola 0.0258883 0.0678598 1.0375547 2
Garrucha -0.6565810 -0.7334459 -0.9645609 1
La Atunara -0.6613315 -0.7503919 -1.0479824 1
Marbella 0.6278290 0.1864821 0.0364969 2
Mazarrón -0.2735663 0.0291260 1.4546621 2
Motril -0.4748577 -0.3654747 -0.7142965 1
Málaga -0.0663126 0.3147879 1.4546621 2
Roquetas de Mar -0.6623482 -0.7552337 -1.0479824 1
Vélez-Málaga 3.0937756 3.1568813 1.3712406 3
Águilas -0.6207216 -0.6898704 -0.6308750 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(sardina_catches)     -2.484467       -0.6211168 -4.466913e-17
## median(sardina_fishingdays) -2.617861       -0.6544652  4.553649e-18
## median(sardina_boats)       -3.336859       -0.8342148  7.806256e-18
##                             sd in category Overall sd      p.value
## median(sardina_catches)         0.06112441  0.9682458 0.0129745461
## median(sardina_fishingdays)     0.14459434  0.9682458 0.0088482855
## median(sardina_boats)           0.17872153  0.9682458 0.0008473081
## 
## $`2`
##                         v.test Mean in category Overall mean sd in category
## median(sardina_boats) 2.672198        0.7574969 7.806256e-18      0.6845481
##                       Overall sd     p.value
## median(sardina_boats)  0.9682458 0.007535622
## 
## $`3`
##                               v.test Mean in category  Overall mean
## median(sardina_fishingdays) 3.260413         3.156881  4.553649e-18
## median(sardina_catches)     3.195238         3.093776 -4.466913e-17
##                             sd in category Overall sd     p.value
## median(sardina_fishingdays)              0  0.9682458 0.001112501
## median(sardina_catches)                  0  0.9682458 0.001397157

3.1.4.2 Hierarchical K-mean clustering

3.1.5 Sardinella

3.1.5.1 Hierarchical PCA clustering

Table 20. Cluster allocation: sardinella - Fish Zone PS-SPF-G1
median(sardinella_catches) median(sardinella_fishingdays) median(sardinella_boats) clust
Adra 0.2270976 0.0344818 0.2704646 3
Algeciras -0.6624274 -0.5355239 -0.5546817 1
Almería 1.7931885 2.5783964 1.9207572 6
Carboneras -0.3812842 -0.4880234 -0.2108707 2
Cartagena -0.6990357 -0.4669121 -0.8297304 1
Estepona -0.3861498 -0.4721900 -0.1421085 2
Fuengirola -0.6338306 -0.5883022 -0.6922061 1
Garrucha -0.5856638 -0.3877447 -0.6922061 1
Marbella -0.5902136 -0.5883022 -0.5546817 1
Mazarrón 2.5741295 2.1772812 2.4708547 7
Motril 0.9074037 0.1611498 -0.4171573 5
Málaga -0.7001285 -0.6305249 -0.6922061 1
Roquetas de Mar -0.6360703 -0.5883022 -0.6922061 1
Vélez-Málaga -0.2681013 -0.1766314 0.8205621 4
Águilas 0.0410860 -0.0288521 -0.0045841 3

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                               v.test Mean in category  Overall mean
## median(sardinella_catches) -2.332786       -0.6439100 -1.017704e-17
## median(sardinella_boats)   -2.436579       -0.6725597  1.214306e-18
##                            sd in category Overall sd    p.value
## median(sardinella_catches)     0.04306348  0.9660918 0.01965940
## median(sardinella_boats)       0.08786110  0.9660918 0.01482693
## 
## $`2`
## NULL
## 
## $`3`
## NULL
## 
## $`4`
## NULL
## 
## $`5`
## NULL
## 
## $`6`
##                                  v.test Mean in category  Overall mean
## median(sardinella_fishingdays) 2.668894         2.578396 -2.127927e-17
## median(sardinella_boats)       1.988173         1.920757  1.214306e-18
##                                sd in category Overall sd    p.value
## median(sardinella_fishingdays)              0  0.9660918 0.00761015
## median(sardinella_boats)                    0  0.9660918 0.04679261
## 
## $`7`
##                                  v.test Mean in category  Overall mean
## median(sardinella_catches)     2.664477         2.574129 -1.017704e-17
## median(sardinella_boats)       2.557578         2.470855  1.214306e-18
## median(sardinella_fishingdays) 2.253700         2.177281 -2.127927e-17
##                                sd in category Overall sd     p.value
## median(sardinella_catches)                  0  0.9660918 0.007710811
## median(sardinella_boats)                    0  0.9660918 0.010540404
## median(sardinella_fishingdays)              0  0.9660918 0.024215036

3.1.5.2 Hierarchical K-mean clustering

3.1.6 Scomber

3.1.6.1 Hierarchical PCA clustering

Table 21. Cluster allocation: scomber - Alboran Sea GSA-01
median(scomber_catches) median(scomber_fishingdays) median(scomber_boats) clust
Adra 0.2363734 0.3418351 0.3874013 2
Algeciras -0.3018260 0.4550377 -0.2143871 1
Almería 3.4544055 3.1593213 1.9520513 3
Carboneras -0.4052795 -0.6602173 -0.4551025 1
Cartagena -0.5044939 -0.5470147 -0.9365333 1
Estepona -0.1948719 -0.2325632 -0.2143871 1
Fuengirola -0.5404907 -0.6895661 -0.4551025 1
Garrucha -0.4684105 -0.5721709 -0.8161756 1
La Atunara -0.5461960 -0.7503601 -0.8763545 1
Marbella -0.5275266 -0.5973270 -0.2143871 1
Mazarrón 0.4836323 0.6520940 2.1927667 2
Motril 0.6338049 0.3669912 -0.5754602 1
Málaga -0.5394037 -0.6266758 0.0263282 1
Roquetas de Mar -0.5131061 -0.5595928 -0.8161756 1
Vélez-Málaga 0.1404042 0.7946454 1.4706206 2
Águilas -0.4070153 -0.5344367 -0.4551025 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(scomber_catches)     -2.491160       -0.3595680  3.122502e-17
## median(scomber_fishingdays) -2.856669       -0.4123246 -1.561251e-17
## median(scomber_boats)       -3.465741       -0.5002367 -1.951564e-18
##                             sd in category Overall sd      p.value
## median(scomber_catches)          0.3170341  0.9682458 0.0127326826
## median(scomber_fishingdays)      0.3878370  0.9682458 0.0042811207
## median(scomber_boats)            0.2979967  0.9682458 0.0005287719
## 
## $`2`
##                         v.test Mean in category  Overall mean sd in category
## median(scomber_boats) 2.594581         1.350263 -1.951564e-18      0.7419346
##                       Overall sd     p.value
## median(scomber_boats)  0.9682458 0.009470624
## 
## $`3`
##                               v.test Mean in category  Overall mean
## median(scomber_catches)     3.567695         3.454405  3.122502e-17
## median(scomber_fishingdays) 3.262933         3.159321 -1.561251e-17
## median(scomber_boats)       2.016070         1.952051 -1.951564e-18
##                             sd in category Overall sd      p.value
## median(scomber_catches)                  0  0.9682458 0.0003601359
## median(scomber_fishingdays)              0  0.9682458 0.0011026556
## median(scomber_boats)                    0  0.9682458 0.0437926620

3.1.6.2 Hierarchical K-mean clustering

3.1.7 Trachurus

3.1.7.1 Hierarchical PCA clustering

Table 22. Cluster allocation: trachurus - Alboran Sea GSA-01
median(trachurus_catches) median(trachurus_fishingdays) median(trachurus_boats) clust
Adra 0.4627764 0.2214175 0.1910003 2
Algeciras 1.2724263 1.1174083 -0.2791543 2
Almería 2.9208032 3.0011295 1.7190028 3
Carboneras -0.5358227 -0.7265591 -0.5142316 1
Cartagena -0.7254322 -0.6348194 -1.1019249 1
Estepona 0.0905028 0.1052139 -0.1616156 2
Fuengirola -0.6998990 -0.5889496 -0.1616156 1
Garrucha -0.4826488 -0.5767176 -0.8668476 1
La Atunara -0.7907740 -0.8198277 -1.1019249 1
Marbella -0.6859642 -0.4360501 -0.0440770 1
Mazarrón 0.4138325 0.0318222 1.8365415 2
Motril 0.4824988 0.1143879 -0.6317703 2
Málaga -0.6867022 -0.4054702 1.2488482 2
Roquetas de Mar -0.7583510 -0.8152408 -0.9843862 1
Vélez-Málaga 0.3645169 1.0073207 1.3663868 2
Águilas -0.6417627 -0.5950655 -0.5142316 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                  v.test Mean in category  Overall mean
## median(trachurus_fishingdays) -2.596615       -0.6491537  1.561251e-17
## median(trachurus_boats)       -2.644620       -0.6611549 -1.517883e-17
## median(trachurus_catches)     -2.660327       -0.6650818 -2.602085e-18
##                               sd in category Overall sd     p.value
## median(trachurus_fishingdays)      0.1225366  0.9682458 0.009414741
## median(trachurus_boats)            0.3887226  0.9682458 0.008178278
## median(trachurus_catches)          0.1002008  0.9682458 0.007806475
## 
## $`2`
## NULL
## 
## $`3`
##                                 v.test Mean in category  Overall mean
## median(trachurus_fishingdays) 3.099553         3.001130  1.561251e-17
## median(trachurus_catches)     3.016593         2.920803 -2.602085e-18
##                               sd in category Overall sd     p.value
## median(trachurus_fishingdays)              0  0.9682458 0.001938127
## median(trachurus_catches)                  0  0.9682458 0.002556332

3.1.7.2 Hierarchical K-mean clustering

3.1.8 CONCLUSION GSA-01

The clustering analysis for anchovy, bogue, sardinella, and mackerel did not reveal a clear mesoscale pattern. Certain harbours, such as Almería, frequently appeared in isolation.

However, the clustering analysis for sardine identified three distinct clusters. In this case, the harbour of Velez-Malaga appeared as a single cluster, while the remaining two clusters displayed a clear east-west division, with the Almería area serving as a reference.

For horse mackerel, the clustering results were similar to those for sardine. However, in this case, Almería appeared alone, while the other harbours were divided into eastern and western groups.

The clustering analysis of the complete dataset of small pelagic landings in the Alboran Sea identified six clusters. Notably, the harbours of Almería, Mazarrón and Vélez-Málaga are not gathered with any other harbour, configuring their own single harbour groups. The remaining three clusters included two particularly interesting groups: one consisting of the neighbouring harbours of Fuengirola and Málaga and another comprising most of the harbours located to the east of Almería’s Bay. This comprehensive analysis of the complete dataset yielded more interpretable results than the individual species analyses, highlighting the relevance of clustering the entire dataset.

Harbours that consistently appeared alone in the species-specific analysis also remained isolated in the complete dataset analysis. In addition, the east-west division persisted across all analyses. The clustering results also enhanced the mesoscale interpretation, as the Fuengirola and Málaga harbours, which consistently clustered together in the species-specific analyses, were grouped into their own cluster here as well.

Fishing days is the variable most influential in the species-specific analyses, except in anchovy and bogue where the fishing days variable has a similar level of influence that the most influence variable. The number of vessels is the variable with less influence, except in bogue where it is the most influential.In contrast, considering the complete dataset, five of the eighth variables with more influence are the number of vessels.

Overall, the results from the total analysis in Alboran Sea GSA-01 suggest common patterns that could reflect the relationship between local markets and how fishermen choose where to land their catches. For instance, the neighbouring clustering of Fuengirola and Málaga indicates a close market relationship, while the isolation of harbours like Almería and Vélez-Málaga suggests distinct market conditions influencing fishing decisions.

3.2 NORTHERN SPAIN GSA-06

3.2.1 Complete dataset

3.2.1.1 Hierarchical PCA clustering

Table 23. Cluster allocation: Complete Dataset Northern Spain GSA-06
median(bogue_catches) median(bogue_fishingdays) median(bogue_boats) median(anchovy_catches) median(anchovy_fishingdays) median(anchovy_boats) median(sardina_catches) median(sardina_fishingdays) median(sardina_boats) median(sardinella_catches) median(sardinella_fishingdays) median(sardinella_boats) median(scomber_catches) median(scomber_fishingdays) median(scomber_boats) median(trachurus_catches) median(trachurus_fishingdays) median(trachurus_boats) clust
Alicante -0.6081746 -0.9680375 -0.9934853 -0.3157100 -0.4454067 1.2094083 -0.2841115 -0.4440688 0.9788611 -0.4607576 -0.4108665 0.2278326 -0.3328048 -0.3124238 1.4360794 -1.0069091 -0.8073319 0.0249455 2
Altea 0.9448213 0.6639031 0.9934853 -0.8188317 -0.3860461 1.0777720 1.1143996 0.1634201 1.4648205 1.8905528 0.9600092 1.8964653 0.3960799 1.2290783 2.1932390 -0.5081258 0.3837174 1.7434102 6
Ametlla de Mar -0.8118126 -0.5229628 0.4967426 -0.3330414 -0.4292175 -1.4891353 -1.2745058 -0.9143828 -1.4509360 -0.7920843 -0.8308458 -1.3124439 -0.9331177 -1.2449374 -1.0373087 -1.0362709 -1.0126853 -1.0283071 1
Arenys de Mar -0.9235984 -0.6713210 -0.9934853 -0.5690874 -0.5749207 0.2221362 -0.3187930 -0.5000585 0.2152106 -0.7753875 -0.8149975 -0.6706620 -0.1131306 -0.1030840 0.0731921 0.5280259 0.1842313 0.3021172 2
Barcelona 0.8864578 2.1474854 2.4837132 0.2519476 0.7418049 0.2879543 1.4906090 1.1544389 0.4234789 0.0306089 0.5162575 0.4845453 1.2644389 1.6350706 0.5779652 1.8388026 2.4489851 1.1890667 5
Blanes 2.9382625 1.8507690 0.9934853 0.5131328 0.0915367 -0.1727726 -0.0918523 0.0374432 -0.2707488 -0.5502426 -0.3078527 -0.5423057 0.7083253 0.3409701 -0.1287171 1.7942360 0.3015760 0.0803798 3
Burriana -0.4642433 -0.9680375 -0.9934853 0.1811417 0.3181862 -0.1069545 -0.3629888 -0.1949144 -0.2707488 -0.6446090 -0.7516044 -0.5423057 -0.6096702 -0.4329527 -0.3306264 -0.4102658 -0.6137131 -0.9174384 2
Cambrils -0.7250142 -0.2262463 -0.4967426 -0.9739002 -0.9904448 -1.6207716 -1.1018214 -0.9647736 -1.5897815 -0.5960679 -0.3078527 -1.1840875 -0.6849519 -0.8516323 -1.3401725 -0.9381534 -0.7779957 -1.2500445 1
Castellón -0.1119772 0.0704702 0.2483713 1.8854996 2.7034023 -0.3702270 1.7090025 2.9083181 -0.3054602 0.8515934 1.1977333 0.9979708 0.1076297 1.4574490 0.3255786 -0.1425752 -0.2147409 0.3021172 4
Gandía 1.5046896 -0.9680375 -0.9934853 -0.5925747 -0.5668261 1.2423173 -1.0909157 -0.7464135 1.0829953 -0.0688910 -0.3157769 0.8696144 -0.8115064 -1.2512810 -0.3306264 -1.1004104 -1.0185525 -0.9728728 2
Jávea -0.1119772 -0.3746045 0.0000000 -0.2745547 -0.3091472 0.7815904 -0.1204033 -0.0829348 0.9441497 0.8733560 0.4370162 0.2278326 -0.1965549 -0.3124238 0.6284425 1.2595571 0.1959657 0.4129859 2
La Escala 0.8149431 1.7024107 0.9934853 -0.4601054 -0.4292175 -0.5676814 0.8961858 0.1746181 -0.4790171 -0.3942445 -0.2444596 -0.6706620 -0.4342012 -0.1665203 -0.9363541 -0.1312810 0.2194347 -0.4739637 3
Palamós 0.0475853 -0.4487837 -0.2483713 -0.6045142 -0.7759372 -0.9625902 -0.5433619 -0.5714455 -1.0343993 -0.7161307 -0.6723630 -0.9915529 -0.7879263 -1.1180648 -0.9868314 -0.1498162 -0.5374390 -0.8620041 1
Roses -0.8845908 0.2188284 -0.9934853 -1.2911581 -1.2589165 -1.4891353 -0.8781914 -1.0207633 -1.4162246 -0.7598640 -0.8229217 -1.1840875 -0.7611077 -0.7691651 -1.3906498 -0.7789114 -0.2323426 -1.1946102 1
San Feliú de Guixols -0.3562151 -0.0037090 -0.2483713 -0.7267356 -0.5722225 -0.0740454 -0.5399515 -0.3894788 -0.0277691 -0.7625412 -0.7119837 -0.4139493 -0.7196144 -0.4456400 0.0227148 0.1410683 -0.4376959 0.0803798 2
San Pedro del Pinatar -0.5769685 -0.9680375 -0.9934853 -1.1619061 -1.2359817 -0.2385907 -1.4031304 -1.3483034 -0.6178627 1.2728866 0.4370162 0.0994762 0.5845483 -0.8135705 -0.7344448 -0.5689551 -0.6723855 -0.6957011 2
Tarragona -0.8298392 -0.8938584 -0.7451140 2.3237315 1.4352444 -0.8967721 0.7597043 1.0340609 -0.8608424 -0.5705662 -0.3474734 -0.2855929 -0.4933720 -0.2236130 -0.6839675 -0.9129933 -0.6137131 -0.5293980 4
Torrevieja 0.5164656 1.1089778 1.4902279 0.9569616 1.1114594 0.7486813 0.6735981 0.7457136 0.8400156 2.7014061 3.3689470 2.4098908 3.2695435 1.9268776 1.6884660 1.0052884 1.3459444 2.2977537 6
Vilanova y la Geltrú -0.3674876 0.0704702 0.4967426 1.1597718 0.8686207 0.4195906 1.7214054 1.1544389 0.5623245 0.0573722 0.2468369 0.4845453 0.9322446 1.3559509 0.2751013 1.6656107 2.3433748 1.0781980 5
Vinaroz -0.8813265 -0.8196792 -0.4967426 0.8499328 0.7040300 1.9992259 -0.3548774 -0.1949144 1.8119344 -0.5863896 -0.6248182 0.0994762 -0.3848523 0.0999122 0.6789198 -0.5479215 -0.4846339 0.4129859 2

The most influential variables in the different clusters are analyzed below and the 8 most influential variables in the cluster analysis are represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(sardina_catches)     -2.123080       -0.9494701 -6.522560e-17
## median(scomber_fishingdays) -2.227012       -0.9959499  4.857226e-18
## median(trachurus_boats)     -2.423320       -1.0837415 -3.504141e-17
## median(sardinella_boats)    -2.611823       -1.1680430  4.302114e-17
## median(scomber_boats)       -2.658105       -1.1887406  1.545639e-16
## median(sardina_boats)       -3.069753       -1.3728354  9.107298e-17
## median(anchovy_boats)       -3.109047       -1.3904081 -1.665335e-17
##                             sd in category Overall sd     p.value
## median(sardina_catches)          0.2733421  0.9746794 0.033747168
## median(scomber_fishingdays)      0.1931099  0.9746794 0.025946494
## median(trachurus_boats)          0.1518132  0.9746794 0.015379389
## median(sardinella_boats)         0.1145810  0.9746794 0.009006075
## median(scomber_boats)            0.1784642  0.9746794 0.007858145
## median(sardina_boats)            0.2059047  0.9746794 0.002142357
## median(anchovy_boats)            0.2527793  0.9746794 0.001876918
## 
## $`2`
##                              v.test Mean in category  Overall mean
## median(anchovy_boats)      2.298192        0.6293859 -1.665335e-17
## median(sardina_catches)   -2.042627       -0.5593965 -6.522560e-17
## median(bogue_boats)       -2.607406       -0.7140675  2.775558e-18
## median(bogue_fishingdays) -2.620608       -0.7176830 -3.423910e-17
##                           sd in category Overall sd     p.value
## median(anchovy_boats)          0.7556444  0.9746794 0.021550837
## median(sardina_catches)        0.4185643  0.9746794 0.041089372
## median(bogue_boats)            0.3815047  0.9746794 0.009123112
## median(bogue_fishingdays)      0.3336772  0.9746794 0.008777322
## 
## $`3`
##                             v.test Mean in category  Overall mean
## median(bogue_catches)     2.797474         1.876603 -9.818535e-17
## median(bogue_fishingdays) 2.648384         1.776590 -3.423910e-17
##                           sd in category Overall sd     p.value
## median(bogue_catches)         1.06165972  0.9746794 0.005150387
## median(bogue_fishingdays)     0.07417912  0.9746794 0.008087766
## 
## $`4`
##                               v.test Mean in category  Overall mean
## median(anchovy_catches)     3.137376         2.104616 -3.053113e-17
## median(anchovy_fishingdays) 3.084765         2.069323 -1.339207e-16
## median(sardina_fishingdays) 2.938476         1.971189 -1.214306e-17
##                             sd in category Overall sd     p.value
## median(anchovy_catches)          0.2191159  0.9746794 0.001704676
## median(anchovy_fishingdays)      0.6340789  0.9746794 0.002037130
## median(sardina_fishingdays)      0.9371286  0.9746794 0.003298304
## 
## $`5`
##                                 v.test Mean in category  Overall mean
## median(trachurus_fishingdays) 3.572014         2.396180  1.665335e-17
## median(trachurus_catches)     2.612035         1.752207 -6.661338e-17
## median(sardina_catches)       2.394094         1.606007 -6.522560e-17
## median(scomber_fishingdays)   2.229376         1.495511  4.857226e-18
## median(bogue_boats)           2.221501         1.490228  2.775558e-18
##                               sd in category Overall sd      p.value
## median(trachurus_fishingdays)     0.05280514  0.9746794 0.0003542462
## median(trachurus_catches)         0.08659596  0.9746794 0.0090004924
## median(sardina_catches)           0.11539818  0.9746794 0.0166614716
## median(scomber_fishingdays)       0.13955986  0.9746794 0.0257889109
## median(bogue_boats)               0.99348527  0.9746794 0.0263170738
## 
## $`6`
##                                  v.test Mean in category  Overall mean
## median(sardinella_catches)     3.422644         2.295979  2.099015e-17
## median(sardinella_fishingdays) 3.226613         2.164478  2.359224e-17
## median(sardinella_boats)       3.209768         2.153178  4.302114e-17
## median(trachurus_boats)        3.012106         2.020582 -3.504141e-17
## median(scomber_boats)          2.893252         1.940852  1.545639e-16
## median(scomber_catches)        2.732194         1.832812  8.187895e-17
## median(scomber_fishingdays)    2.352311         1.577978  4.857226e-18
##                                sd in category Overall sd      p.value
## median(sardinella_catches)          0.4054266  0.9746794 0.0006201522
## median(sardinella_fishingdays)      1.2044689  0.9746794 0.0012526457
## median(sardinella_boats)            0.2567127  0.9746794 0.0013284197
## median(trachurus_boats)             0.2771717  0.9746794 0.0025944224
## median(scomber_boats)               0.2523865  0.9746794 0.0038127517
## median(scomber_catches)             1.4367318  0.9746794 0.0062914015
## median(scomber_fishingdays)         0.3488996  0.9746794 0.0186571871

3.2.1.2 Hierarchical K-mean clustering

3.2.2 Anchovy

3.2.2.1 Hierarchical PCA clustering

Table 24. Cluster allocation: Anchovy - Northern Spain GSA-06
median(anchovy_catches) median(anchovy_fishingdays) median(anchovy_boats) clust
Alicante -0.0313749 0.2702213 1.5492565 3
Altea -0.0631407 0.6043162 1.9879223 3
Ametlla de Mar -0.8481808 -0.8238220 -0.8947391 1
Arenys de Mar 0.0289835 -0.0450514 0.4839251 2
Badalona -1.0139188 -1.0696876 -1.2394052 1
Barcelona 0.5402013 1.1031058 0.7972578 3
Benicarló -1.0174295 -1.0708640 -1.2707385 1
Blanes 0.7459238 0.5360855 0.3585919 3
Burriana 0.9280545 0.5007938 0.4212585 3
Calpe -1.0159810 -1.0696876 -1.2394052 1
Cambrils -0.4253373 -0.4073797 -1.0200722 1
Castellón 2.7274038 2.7900499 0.0452592 4
Denia -1.0177203 -1.0685112 -1.2707385 1
Gandía 0.2221361 0.0114153 1.3612568 3
Jávea 0.1930153 0.2584574 1.2985903 3
La Escala -0.0206470 0.0819988 -0.0174074 2
Palamós -0.1460075 -0.2238628 -0.3934067 2
Roses -0.5065327 -0.3838519 -0.5814064 2
Sagunto -1.0021978 -1.0591001 -1.2707385 1
San Feliú de Guixols -0.1896660 -0.0921070 0.3585919 2
San Pedro del Pinatar -0.5734201 -0.6214828 0.2959254 2
Tarragona 2.3193919 1.9806932 -0.3307401 4
Torrevieja 1.0955055 1.0772252 1.2985903 3
Valencia -0.9108722 -0.9344027 -0.5187398 1
Vilanova y la Geltrú 1.2552489 1.2136865 0.9225909 3
Villajoyosa -1.0138721 -1.0685112 -1.2394052 1
Vinaroz -0.2595657 -0.4897270 0.1079257 2

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(anchovy_catches)     -3.374380       -0.9183900  5.756712e-17
## median(anchovy_fishingdays) -3.499490       -0.9524407 -2.621360e-17
## median(anchovy_boats)       -4.067779       -1.1071091 -4.073388e-17
##                             sd in category Overall sd      p.value
## median(anchovy_catches)          0.1833492  0.9813068 7.398208e-04
## median(anchovy_fishingdays)      0.2091778  0.9813068 4.661483e-04
## median(anchovy_boats)            0.2430558  0.9813068 4.746344e-05
## 
## $`2`
## NULL
## 
## $`3`
##                               v.test Mean in category  Overall mean
## median(anchovy_boats)       4.080570        1.1105906 -4.073388e-17
## median(anchovy_fishingdays) 2.276110        0.6194786 -2.621360e-17
## median(anchovy_catches)     1.994526        0.5428411  5.756712e-17
##                             sd in category Overall sd      p.value
## median(anchovy_boats)            0.5022020  0.9813068 4.492532e-05
## median(anchovy_fishingdays)      0.3999776  0.9813068 2.283945e-02
## median(anchovy_catches)          0.4624535  0.9813068 4.609466e-02
## 
## $`4`
##                               v.test Mean in category  Overall mean
## median(anchovy_catches)     3.708622         2.523398  5.756712e-17
## median(anchovy_fishingdays) 3.505766         2.385372 -2.621360e-17
##                             sd in category Overall sd      p.value
## median(anchovy_catches)          0.2040059  0.9813068 0.0002083900
## median(anchovy_fishingdays)      0.4046784  0.9813068 0.0004552956

3.2.2.2 Hierarchical K-mean clustering

3.2.3 Bogue

3.2.3.1 Hierarchical PCA clustering

Table 25. Cluster allocation: bogue - Northern Spain GSA-06
median(bogue_catches) median(bogue_fishingdays) median(bogue_boats) clust
Alicante -0.6627588 -0.9634137 -0.9818058 1
Altea 0.9168231 0.6732737 1.0321548 4
Ametlla de Mar -0.8746602 -0.6658342 0.0251745 1
Arenys de Mar -0.4060082 -0.5170444 -0.7300607 1
Barcelona 0.8574605 2.1611713 2.5426253 4
Blanes 2.9443905 1.8635918 1.0321548 5
Burriana -0.5163635 -0.9634137 -0.9818058 1
Cambrils -0.7815986 -0.2194649 -0.4783156 1
Castellón -0.1580669 0.0781146 0.2769196 3
Gandía 1.4862760 -0.9634137 -0.9818058 2
Jávea -0.1580669 -0.3682547 0.0251745 3
La Escala 0.7847215 1.7148020 1.0321548 4
Palamós 0.3148847 -0.3682547 -0.4783156 3
Roses -0.9439070 0.0037197 -0.9818058 1
San Feliú de Guixols -0.4064859 0.0037197 -0.2265706 3
San Pedro del Pinatar -0.6310185 -0.9634137 -0.9818058 1
Tarragona -0.8882182 -0.8890188 -0.7300607 1
Torrevieja 0.4811344 1.1196430 1.5356450 4
Vilanova y la Geltrú -0.4179514 0.0781146 0.5286647 3
Vinaroz -0.9405868 -0.8146240 -0.4783156 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                              v.test Mean in category  Overall mean
## median(bogue_fishingdays) -2.693432       -0.6658342  5.928417e-17
## median(bogue_boats)       -2.840090       -0.7020890 -1.131040e-16
## median(bogue_catches)     -2.986759       -0.7383466 -8.049117e-17
##                           sd in category Overall sd     p.value
## median(bogue_fishingdays)      0.3345473  0.9746794 0.007072058
## median(bogue_boats)            0.3237956  0.9746794 0.004510080
## median(bogue_catches)          0.1834933  0.9746794 0.002819517
## 
## $`2`
## NULL
## 
## $`3`
## NULL
## 
## $`4`
##                             v.test Mean in category  Overall mean
## median(bogue_boats)       3.433807         1.535645 -1.131040e-16
## median(bogue_fishingdays) 3.169006         1.417223  5.928417e-17
##                           sd in category Overall sd      p.value
## median(bogue_boats)            0.6166470  0.9746794 0.0005951688
## median(bogue_fishingdays)      0.5665745  0.9746794 0.0015296131
## 
## $`5`
##                         v.test Mean in category  Overall mean sd in category
## median(bogue_catches) 3.020881         2.944391 -8.049117e-17              0
##                       Overall sd     p.value
## median(bogue_catches)  0.9746794 0.002520404

3.2.3.2 Hierarchical K-mean clustering

3.2.4 Sardine

3.2.4.1 Hierarchical PCA clustering

Table 26. Cluster allocation: sardine - Northern Spain GSA-06
median(sardina_catches) median(sardina_fishingdays) median(sardina_boats) clust
Alicante -0.2645753 -0.0012649 1.2630359 2
Altea 0.5351997 0.6284249 1.9479795 2
Ametlla de Mar -0.9733172 -0.9856708 -0.9972778 1
Arenys de Mar 0.1708731 0.1279728 0.4411037 2
Badalona -1.1217979 -1.1932759 -1.3739968 1
Barcelona 1.9035349 1.5578361 0.8520698 3
Benicarló -1.1226172 -1.1946508 -1.4082440 1
Blanes 0.3881887 0.4606910 0.1671262 2
Burriana -0.1231294 0.1197236 0.1671262 2
Cambrils -0.5789457 -0.5237149 -1.1342665 1
Castellón 1.4105372 2.3525101 0.0301375 3
Gandía -0.4888740 -0.4137255 1.3315303 2
Jávea 0.1313977 0.0757278 1.3315303 2
La Escala 1.3343223 0.5954281 -0.0383568 3
Mataró -1.1234328 -1.1946508 -1.4082440 1
Palamós -0.0450690 -0.1937465 -0.5863117 1
Roses -0.0686500 -0.3477318 -0.7233004 1
San Feliú de Guixols -0.2704891 -0.0837570 0.3041149 2
San Pedro del Pinatar -0.8674756 -0.9004289 -0.1753456 1
Tarragona 0.9888808 1.2911116 -0.3808286 3
Torrevieja 1.1211750 1.1481253 1.2630359 3
Valencia -1.0608721 -1.0750372 -0.5863117 1
Vilanova y la Geltrú 2.1245428 1.5578361 0.9890585 3
Villajoyosa -1.1225049 -1.1932759 -1.3739968 1
Vinaroz -0.8769021 -0.6144563 0.0986319 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category  Overall mean
## median(sardina_catches)     -3.610725       -0.8146895 -5.273559e-17
## median(sardina_fishingdays) -3.794072       -0.8560582  3.826800e-17
## median(sardina_boats)       -3.895615       -0.8789694  1.151856e-16
##                             sd in category Overall sd      p.value
## median(sardina_catches)          0.3903494  0.9797959 0.0003053420
## median(sardina_fishingdays)      0.3553894  0.9797959 0.0001481965
## median(sardina_boats)            0.5023455  0.9797959 0.0000979497
## 
## $`2`
##                         v.test Mean in category Overall mean sd in category
## median(sardina_boats) 2.981309        0.8691934 1.151856e-16      0.6358826
##                       Overall sd     p.value
## median(sardina_boats)  0.9797959 0.002870193
## 
## $`3`
##                               v.test Mean in category  Overall mean
## median(sardina_catches)     4.159843         1.480499 -5.273559e-17
## median(sardina_fishingdays) 3.981823         1.417141  3.826800e-17
##                             sd in category Overall sd      p.value
## median(sardina_catches)          0.4063694  0.9797959 3.184670e-05
## median(sardina_fishingdays)      0.5288306  0.9797959 6.838869e-05

3.2.4.2 Hierarchical K-mean clustering

3.2.5 Sardinella

3.2.5.1 Hierarchical PCA clustering

Table 27. Cluster allocation: sardinella - Northern Spain GSA-06
median(sardinella_catches) median(sardinella_fishingdays) median(sardinella_boats) clust
Alicante -0.1370805 0.1009348 1.3457869 2
Altea 0.9027827 0.7353820 2.0906250 2
Ametlla de Mar -0.4772736 -0.3363194 -0.3921685 1
Arenys de Mar -0.5498037 -0.6192485 -0.2680288 1
Barcelona 0.3643530 0.7868236 0.7250886 2
Blanes -0.3655032 -0.1048319 -0.2680288 1
Burriana -0.5571351 -0.7221318 -0.9507971 1
Cambrils -0.4230840 -0.1048319 -0.8887272 1
Castellón -0.5040117 -0.5335124 -0.0197495 1
Denia -0.6989731 -0.7907207 -1.1370066 1
Gandía -0.5212839 -0.4134819 0.2285299 1
Jávea 0.7736484 0.1695237 0.6009489 2
La Escala -0.1694873 -0.0362430 -0.3921685 1
Palamós -0.5183950 -0.3963346 -0.6404479 1
Roses -0.5856831 -0.6535429 -0.8887272 1
San Feliú de Guixols -0.6227698 -0.5506596 -0.6404479 1
San Pedro del Pinatar 1.9253097 0.7010875 0.1043902 2
Tarragona -0.4041119 -0.1734208 -0.1438892 1
Torrevieja 3.3850489 3.8733234 2.5871837 3
Valencia -0.6926397 -0.7564263 -0.8887272 1
Vilanova y la Geltrú 0.3979818 0.4953209 0.7250886 2
Vinaroz -0.5218889 -0.6706902 -0.8887272 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                   v.test Mean in category  Overall mean
## median(sardinella_fishingdays) -3.141177       -0.4574931 -5.141090e-17
## median(sardinella_catches)     -3.484320       -0.5074696  7.569702e-18
## median(sardinella_boats)       -3.743888       -0.5452741  1.026641e-16
##                                sd in category Overall sd      p.value
## median(sardinella_fishingdays)      0.2479512  0.9770084 0.0016827011
## median(sardinella_catches)          0.1286504  0.9770084 0.0004933901
## median(sardinella_boats)            0.3865422  0.9770084 0.0001811945
## 
## $`2`
##                              v.test Mean in category Overall mean
## median(sardinella_boats)   2.676932        0.9319880 1.026641e-16
## median(sardinella_catches) 2.023520        0.7044992 7.569702e-18
##                            sd in category Overall sd     p.value
## median(sardinella_boats)        0.6316366  0.9770084 0.007429976
## median(sardinella_catches)      0.6393206  0.9770084 0.043019585
## 
## $`3`
##                                  v.test Mean in category  Overall mean
## median(sardinella_fishingdays) 3.964473         3.873323 -5.141090e-17
## median(sardinella_catches)     3.464708         3.385049  7.569702e-18
## median(sardinella_boats)       2.648067         2.587184  1.026641e-16
##                                sd in category Overall sd      p.value
## median(sardinella_fishingdays)              0  0.9770084 0.0000735583
## median(sardinella_catches)                  0  0.9770084 0.0005308076
## median(sardinella_boats)                    0  0.9770084 0.0080953496

3.2.5.2 Hierarchical K-mean clustering

3.2.6 Scomber

3.2.6.1 Hierarchical PCA clustering

Table 28. Cluster allocation: scomber - Northern Spain GSA-06
median(scomber_catches) median(scomber_fishingdays) median(scomber_boats) clust
Alicante -0.0729376 0.1608000 1.5324701 2
Altea -0.2191714 0.4695361 2.3980126 2
Ametlla de Mar -0.6689751 -0.9455043 -0.7756431 1
Arenys de Mar 0.0617355 0.2122561 0.2341564 2
Barcelona 1.6299621 2.0775366 0.8592704 3
Blanes 1.0380413 0.7654082 0.1860707 2
Burriana -0.5291381 -0.2379841 -0.1986148 1
Calpe -0.7350629 -1.1513283 -1.2565001 1
Cambrils -0.4449465 -0.4438081 -0.9679859 1
Castellón -0.3825356 0.8168642 0.3784135 2
Denia -0.7199181 -1.0484163 -1.2565001 1
Gandía -0.5634415 -0.6367682 0.0898993 1
Jávea -0.2528549 -0.1736640 0.6669276 2
La Escala -0.1780503 0.2508481 -0.5833004 1
Palamós -0.5815677 -0.7139522 -0.7756431 1
Roses -0.5749166 -0.5338561 -0.5833004 1
San Feliú de Guixols -0.5045345 -0.2894401 0.2822421 2
San Pedro del Pinatar 0.9062945 -0.4052161 -0.3909576 1
Tarragona -0.4069913 -0.3280321 -0.2947862 1
Torrevieja 3.3356757 2.3734087 1.9171557 3
Valencia -0.7216391 -1.1448963 -1.2084144 1
Vilanova y la Geltrú 1.2763784 1.7945285 0.5707563 3
Vinaroz -0.6914066 -0.8683202 -0.8237288 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                v.test Mean in category Overall mean
## median(scomber_catches)     -2.485776       -0.4545969 1.960992e-17
## median(scomber_fishingdays) -3.452145       -0.6313257 2.413528e-18
## median(scomber_boats)       -3.796316       -0.6942673 2.353190e-17
##                             sd in category Overall sd      p.value
## median(scomber_catches)          0.4207383  0.9780193 0.0129269223
## median(scomber_fishingdays)      0.3913072  0.9780193 0.0005561480
## median(scomber_boats)            0.4055654  0.9780193 0.0001468624
## 
## $`2`
##                         v.test Mean in category Overall mean sd in category
## median(scomber_boats) 2.573195        0.8111847  2.35319e-17      0.7791832
##                       Overall sd    p.value
## median(scomber_boats)  0.9780193 0.01007644
## 
## $`3`
##                               v.test Mean in category Overall mean
## median(scomber_fishingdays) 3.866818         2.081825 2.413528e-18
## median(scomber_catches)     3.864677         2.080672 1.960992e-17
## median(scomber_boats)       2.072372         1.115727 2.353190e-17
##                             sd in category Overall sd      p.value
## median(scomber_fishingdays)      0.2363463  0.9780193 0.0001102649
## median(scomber_catches)          0.8990851  0.9780193 0.0001112364
## median(scomber_boats)            0.5788065  0.9780193 0.0382307859

3.2.6.2 Hierarchical K-mean clustering

3.2.7 Trachurus

3.2.7.1 Hierarchical PCA clustering

Table 29. Cluster allocation: trachurus - Northern Spain GSA-06
median(trachurus_catches) median(trachurus_fishingdays) median(trachurus_boats) clust
Alicante -0.7170754 -0.4342895 0.9077301 2
Altea -0.3410177 0.5823077 1.9516196 2
Ametlla de Mar -0.7197562 -0.6825283 -0.7160982 1
Arenys de Mar 0.8049060 0.2867852 0.4437791 2
Barcelona 2.2252280 2.7218901 1.6036564 3
Benicarló -0.8442622 -0.8362000 -1.1800491 1
Blanes 2.1795777 0.5586659 0.4437791 3
Burriana -0.5361237 -0.5997820 -0.6001104 1
Cambrils -0.6192528 -0.5288567 -0.9480736 1
Castellón -0.5393359 -0.5406776 -0.2521472 1
Denia -0.8471755 -0.8362000 -1.1800491 1
Gandía -0.8085111 -0.7298119 -0.6001104 1
Jávea -0.2152436 -0.1978715 0.2118037 2
La Escala 0.2072396 0.4759196 -0.1361595 2
Palamós 0.2734312 -0.3988268 -0.6001104 1
Roses -0.5439422 -0.2096924 -0.6001104 1
San Feliú de Guixols 0.4141353 -0.2096924 0.2118037 2
San Pedro del Pinatar -0.3955769 -0.4697522 -0.3681350 1
Tarragona -0.6980879 -0.4579313 -0.2521472 1
Torrevieja 1.0128735 1.2560988 2.0676073 3
Valencia -0.8475379 -0.8362000 -1.1800491 1
Vilanova y la Geltrú 2.0478248 2.6155020 1.4876687 3
Vinaroz -0.4923170 -0.5288567 -0.7160982 1

The influence of the variables on the clusters is then analyzed and the influence of each variable on the cluster analysis is represented.

## $`1`
##                                  v.test Mean in category  Overall mean
## median(trachurus_catches)     -3.204489       -0.5860345 -2.654881e-17
## median(trachurus_fishingdays) -3.219996       -0.5888704 -2.896234e-17
## median(trachurus_boats)       -3.866902       -0.7071760 -6.757879e-17
##                               sd in category Overall sd      p.value
## median(trachurus_catches)          0.2874930  0.9780193 0.0013530256
## median(trachurus_fishingdays)      0.1825089  0.9780193 0.0012819231
## median(trachurus_boats)            0.3167043  0.9780193 0.0001102269
## 
## $`2`
## NULL
## 
## $`3`
##                                 v.test Mean in category  Overall mean
## median(trachurus_catches)     4.106920         1.866376 -2.654881e-17
## median(trachurus_fishingdays) 3.934542         1.788039 -2.896234e-17
## median(trachurus_boats)       3.082161         1.400678 -6.757879e-17
##                               sd in category Overall sd      p.value
## median(trachurus_catches)          0.4970564  0.9780193 4.009698e-05
## median(trachurus_fishingdays)      0.9152993  0.9780193 8.335561e-05
## median(trachurus_boats)            0.5935524  0.9780193 2.055033e-03

3.2.7.2 Hierarchical K-mean clustering

3.2.8 CONCLUSION GSA-06

The PCA clustering analysis results for anchovy identified four distinct groups, with one cluster containing the harbours of Castellón and Tarragona, which are in the vicinity of the Ebro River Delta. The remaining three clusters comprised smaller groups of neighbouring harbours. These patterns were also confirmed by the k-means clustering analysis, which identified six clusters. In one cluster, four harbours from the southern Valencia Community were grouped, while another cluster included the Catalonian harbours of Cambrils, Blanes, and Palamós.

The clustering analysis results for Bogue identified groups which do not provide a clear mesoscale interpretation, with several harbours appearing isolated.

The PCA clustering analysis results for sardine determined three groups, one of which contained harbours near the Ebro Delta, indicating a possible mesoscale fishing area. The other two clusters suggested smaller groups of neighbouring harbours. The k-means analysis, with six clusters, introduced two interesting grouping structures: one with two harbours in southern Alicante, and another with the neighbouring harbours of Barcelona and Vilanova y la Geltrú.

The PCA clustering analysis results for sardinella did not show a clear mesoscale pattern, as the harbour of Torrevieja appeared isolated. However, the k-means identified six groups, three of which consisted of single-port clusters in the southern area, suggesting possible mesoscale fishing patterns.

The PCA clustering analysis results for scomber showed three groups without clear mesoscale patterns, except for the neighbouring harbours of Barcelona and Vilanova i la Geltrú with Torrevieja. In the k-means analysis, two groups of neighbouring harbours were identified: one with Barcelona and Vilanova i la Geltrú, and another with Alicante and Altea. The harbour of Torrevieja appeared as a single-port group in the k-means results.

The PCA clustering analysis for horse mackerel also did not reveal clear mesoscale patterns. In the k-means analysis, six clusters were identified, with Torrevieja and Blanes forming single-port clusters. Two groups of neighbouring harbours were detected: one with Barcelona and Vilanova i la Geltrú, and another with Alicante and Altea.

For the complete dataset (Figure 8) in the fishing area GSA-06, the PCA clustering analysis identified six clusters, with Tarragona standing. Two of the clusters grouped most of the northern Catalonian harbours. Other two cluster groups neighbouring harbours, one with Alicante and Altea and one with Barcelona and Vilanova i la Geltrú. The latter cluster also included Castellón, suggesting a possible mesoscale fishing area around the Ebro Delta. The k-means analysis for the complete dataset differed slightly, placing Castellón with Tarragona.

Fishing days is the variable most influential in the species-specific analyses while the number of vessels is the variable with less influence, except in bogue where it is the second. In contrast, considering the complete dataset, we observed again a bigger influence of the number of vessels but, in this case, with a similar level of influence of fishing days.

The overall results for the GSA-06 fishing area highlight several potential mesoscale fishing zones: Catalonia, the Gulf of Valencia, and the Gulf of Alicante. Within Catalonia, three smaller sub-areas were identified: one in southern Catalonia around the Ebro Delta, another in the north around the Gulf of Roses, and a third in central Catalonia, near Barcelona province. The clustering consistently grouped neighbouring harbours, such as Barcelona and Vilanova i la Geltrú. The relationship between Alicante, Torrevieja, and Altea suggests a more complex situation, where fishermen may adjust their landing locations based on market conditions.

4 ACKNOWLEDGEMENTS

Landings and fishing effort data collection has been co-funded by the EU through the European Maritime and Fisheries Fund (EMFF) within the National Program of collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy.

5 R CODE

# ==============================
# R Librarys
# ==============================

if(!(require(readxl))) install.packages("readxl", dep=TRUE)
if(!(require(dplyr))) install.packages("dplyr", dep=TRUE)
if(!(require(knitr))) install.packages("knitr", dep=TRUE)
if(!(require(kableExtra))) install.packages("kableExtra", dep=TRUE)
if (!(require(dlookr))) install.packages("dlookr", dep=TRUE)
if (!(require(purrr))) install.packages("purrr", dep=TRUE)
if (!(require(DataExplorer))) install.packages("DataExplorer", dep=TRUE)
if (!(require(DataExplorer))) install.packages("patchwork", dep=TRUE)
if (!(require(DataExplorer))) install.packages("GGally", dep=TRUE)


# ==============================
# Load data
# ==============================

# Load data
data <- as.data.frame(read_excel("C:/Users/Rober & Laura/Desktop/Paper IEO-UOC/data.xlsx"))

# Exclude data from years 2020 and 2021
data <- subset(data, year < 2020)

# Select fish zones
data_G1 <- subset(data, fisherie_area == "PS-SPF-G1")
data_G6 <- subset(data, fisherie_area == "PS-SPF-G6")

#Load data2
data2 <- as.data.frame(read_excel("C:/Users/Rober & Laura/Desktop/Paper IEO-UOC/data2.xlsx"))

# Exclude data from years 2020 $ 2021
data2 <- subset(data2, year < 2020)

# Select fish zones
data2_G1 <- subset(data2, fisherie_area == "PS-SPF-G1")
data2_G6 <- subset(data2, fisherie_area == "PS-SPF-G6")

# Dataset for each species - Fish zone G1
bogue_G1 <- na.omit(data_G1[, c(1:3, 5:7)])
anchovy_G1 <- na.omit(data_G1[,c(1:3, 8:10)])
sardine_G1 <- na.omit(data_G1[,c(1:3, 11:13)])
sardinella_G1 <- na.omit(data_G1[,c(1:3, 14:16)])
scomber_G1 <- na.omit(data_G1[,c(1:3, 17:19)])
trachurus_G1 <- na.omit(data_G1[,c(1:3, 20:22)])

# Dataset for each species - Fish zone G6
bogue_G6 <- na.omit(data_G6[, c(1:3, 5:7)])
anchovy_G6 <- na.omit(data_G6[,c(1:3, 8:10)])
sardine_G6 <- na.omit(data_G6[,c(1:3, 11:13)])
sardinella_G6 <- na.omit(data_G6[,c(1:3, 14:16)])
scomber_G6 <- na.omit(data_G6[,c(1:3, 17:19)])
trachurus_G6 <- na.omit(data_G6[,c(1:3, 20:22)])


# Database example
data[data$year==2009 & data$fisherie_area=='PS-SPF-G1', c(1:3, 8:13)] %>% kbl(caption = "Table 1. Example of dataset contain. Fishing area Alboran Sea GSA-01 in 2009. Species: Sardine and anchovy") %>% kable_classic(full_width = T, html_font = "Calibri")

# ==============================
# Description
# ==============================

# Variables description - Fish zone GSA-01
dscrpton_spG1 <- data2_G1 %>% group_by(species, year) %>% describe() %>% dplyr::select(described_variables, species, year, n, na, mean, sd, se_mean, IQR, skewness, kurtosis)
dscrpton_spG1 %>% kbl(caption = "Table 2. Alboran Sea GSA-01: Variables description per species and year") %>% kable_classic(full_width = T, html_font = "calibri")

# Boxplot per species for each variable - Fish zone GSA-01
p1 <- ggplot(subset(data2_G1, species %in% c("bogue")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p2 <- ggplot(subset(data2_G1, species %in% c("anchovy", "sardinella", "scomber",  "trachurus")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p3 <- ggplot(subset(data2_G1, species %in% c("sardine")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p4 <- ggplot(subset(data2_G1, species %in% c("anchovy", "scomber", "trachurus")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p5 <- ggplot(subset(data2_G1, species %in% c("bogue", "sardinella")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p6 <- ggplot(subset(data2_G1, species %in% c("sardine")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p7 <- ggplot(data2_G1, mapping = aes(x = species, y = boats)) +  geom_boxplot()
design <- "ABBBC
           DDEEF
           GGGGG"
library(patchwork)
wrap_plots(list(A=p1, B=p2, C=p3, D=p4, E=p5, F=p6, G=p7), design = design)

# Variables description - Fish zone GSA-6
dscrpton_spG6 <- data2_G6 %>% group_by(species, year) %>% describe() %>% dplyr::select(described_variables, species, year, n, na, mean, sd, se_mean, IQR, skewness, kurtosis)
dscrpton_spG6 %>% kbl(caption = "Table 3. Northern Spain GSA-06: Variables description per species and year") %>% kable_classic(full_width = T, html_font = "calibri")

# Boxplot per species for each variable - Fish zone GSA-06
p1 <- ggplot(subset(data2_G6, species %in% c("anchovy", "sardine")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p2 <- ggplot(subset(data2_G6, species %in% c("bogue")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p3 <- ggplot(subset(data2_G6, species %in% c("sardinella")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p4 <- ggplot(subset(data2_G6, species %in% c("scomber", "trachurus")), mapping = aes(x = species, y = catches)) +  geom_boxplot()
p5 <- ggplot(subset(data2_G6, species %in% c("anchovy", "sardine")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p6 <- ggplot(subset(data2_G6, species %in% c("bogue")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p7 <- ggplot(subset(data2_G6, species %in% c("sardinella", "scomber", "trachurus")), mapping = aes(x = species, y = fishingdays)) +  geom_boxplot()
p8 <- ggplot(data2_G6, mapping = aes(x = species, y = boats)) +  geom_boxplot()
design <- "AABCDD
           EEFGGG
           HHHHHH"
wrap_plots(list(A=p1, B=p2, C=p3, D=p4, E=p5, F=p6, G=p7, H=p8), design = design)

# NA graphic
plot_missing(data)

# NA omit
data <- na.omit(data)
data_G1 <- na.omit(data_G1)
data_G6 <- na.omit(data_G6)
data2 <- na.omit(data2)
data2_G1 <- na.omit(data2_G1)
data2_G6 <- na.omit(data2_G6)

# Summary of variables per species and year - Fish zone GSA-01
library(GGally)
anchovy_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 4. Statistical descriptors for anchovy in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(anchovy_G1, columns = 4:6, cardinality_threshold = 50)
bogue_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 5. Statistical descriptors for bogue in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(bogue_G1, columns = 4:6, cardinality_threshold = 50)
sardine_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 6. Statistical descriptors for sardine in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(sardine_G1, columns = 4:6,  cardinality_threshold = 50)
sardinella_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 7. Statistical descriptors for sardinella in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(sardinella_G1, columns = 4:6,  cardinality_threshold = 50)
scomber_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 8. Statistical descriptors for scomber in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(scomber_G1, columns = 4:6,  cardinality_threshold = 50)
trachurus_G1[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 9. Statistical descriptors for trachurus in Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(trachurus_G1, columns = 4:6,  cardinality_threshold = 50)

# Summary of variabes per species and year - Fish zone GSA-06
anchovy_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 10. Statistical descriptors for anchovy in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(anchovy_G6, columns = 4:6,  cardinality_threshold = 50)
bogue_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 11. Statistical descriptors for bogue in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(bogue_G6, columns = 4:6,  cardinality_threshold = 50)
sardine_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 12. Statistical descriptors for sardine in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(sardine_G6, columns = 4:6,  cardinality_threshold = 50)
sardinella_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 13. Statistical descriptors for sardinella in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(sardinella_G6, columns = 4:6,  cardinality_threshold = 50)
scomber_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 14. Statistical descriptors for scomber in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(scomber_G6, columns = 4:6,  cardinality_threshold = 50)
trachurus_G6[,c(2, 4:6)] %>% split(.$year) %>% purrr::map(summary) %>% kbl(caption = "Table 15. Statistical descriptors for trachurus in Northen Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
ggpairs(trachurus_G6, columns = 4:6,  cardinality_threshold = 50)

# Count harbour for data and each specie data- Fish zone GSA-01
data_G1 %>% count(harbour) %>% count(n < 4)
anchovy_G1 %>% count(harbour) %>% count(n < 4)
bogue_G1 %>% count(harbour) %>% count(n < 4)
sardine_G1 %>% count(harbour) %>% count(n < 4)
sardinella_G1 %>% count(harbour) %>% count(n < 4)
scomber_G1 %>% count(harbour) %>% count(n < 4)
trachurus_G1 %>% count(harbour) %>% count(n < 4)

# Count harbour for data and each specie data- Fish zone GSA-06
data_G6 %>% count(harbour) %>% count(n < 4)
anchovy_G6 %>% count(harbour) %>% count(n < 4)
bogue_G6 %>% count(harbour) %>% count(n < 4)
sardine_G6 %>% count(harbour) %>% count(n < 4)
sardinella_G6 %>% count(harbour) %>% count(n < 4)
scomber_G6 %>% count(harbour) %>% count(n < 4)
trachurus_G6 %>% count(harbour) %>% count(n < 4)


#Erase harbours (n<4) fish zone GSA-01
data_G1 <- data_G1[!(data_G1$harbour == "Alicante") & !(data_G1$harbour == "Ametlla de Mar") & !(data_G1$harbour == "Gandía") & !(data_G1$harbour == "Vinaroz"),]
anchovy_G1 <- anchovy_G1[!(anchovy_G1$harbour == "Alicante") & !(anchovy_G1$harbour == "Badalona") & !(anchovy_G1$harbour == "Benicarló") & !(anchovy_G1$harbour == "Calpe") & !(anchovy_G1$harbour == "Denia") & !(anchovy_G1$harbour == "Sagunto") & !(anchovy_G1$harbour == "Villajoyosa"),]
bogue_G1 <- bogue_G1[!(bogue_G1$harbour == "Alicante") & !(bogue_G1$harbour == "Ametlla de Mar") & !(bogue_G1$harbour == "Gandía") & !(bogue_G1$harbour == "Vinaroz"),]
sardine_G1 <- sardine_G1[!(sardine_G1$harbour == "Alicante") & !(sardine_G1$harbour == "Badalona") & !(sardine_G1$harbour == "Benicarló") & !(sardine_G1$harbour == "Mataró") & !(sardine_G1$harbour == "Villajoyosa"),]
sardinella_G1 <- sardinella_G1[!(sardinella_G1$harbour == "Alicante") &  !(sardinella_G1$harbour == "Denia") & !(sardinella_G1$harbour == "Valencia"),]
scomber_G1 <- scomber_G1[!(scomber_G1$harbour == "Alicante") & !(scomber_G1$harbour == "Benicarló") & !(scomber_G1$harbour == "Calpe") &  !(scomber_G1$harbour == "Denia") & !(scomber_G1$harbour == "Valencia"),]
trachurus_G1 <- trachurus_G1[!(trachurus_G1$harbour == "Alicante") &  !(trachurus_G1$harbour == "Benicarló") & !(trachurus_G1$harbour == "Valencia"),]

#Erase harbours (n<4) fish zone GSA-06
data_G6 <- data_G6[!(data_G6$harbour == "Roquetas de Mar"),]
anchovy_G6 <- anchovy_G6[!(anchovy_G6$harbour == "La Atunara") & !(anchovy_G6$harbour == "Roquetas de Mar"),]
scomber_G6 <- scomber_G6[!(scomber_G6$harbour == "La Atunara") & !(scomber_G6$harbour == "Roquetas de Mar"),]


# Median of variables fish zone GSA-01
data_G1_med <- data_G1 %>% group_by(harbour) %>% summarise(median(bogue_catches), median(bogue_fishingdays), median(bogue_boats), median(anchovy_catches), median(anchovy_fishingdays), median(anchovy_boats), median(sardina_catches), median(sardina_fishingdays), median(sardina_boats), median(sardinella_catches), median(sardinella_fishingdays), median(sardinella_boats), median(scomber_catches), median(scomber_fishingdays), median(scomber_boats), median(trachurus_catches), median(trachurus_fishingdays), median(trachurus_boats))
anchovy_G1_med <- anchovy_G1 %>% group_by(harbour) %>% summarise(median(anchovy_catches), median(anchovy_fishingdays), median(anchovy_boats))
bogue_G1_med <- bogue_G1 %>% group_by(harbour) %>% summarise(median(bogue_catches), median(bogue_fishingdays), median(bogue_boats))
sardine_G1_med <- sardine_G1 %>% group_by(harbour) %>% summarise(median(sardina_catches), median(sardina_fishingdays), median(sardina_boats))
sardinella_G1_med <- sardinella_G1 %>% group_by(harbour) %>% summarise(median(sardinella_catches), median(sardinella_fishingdays), median(sardinella_boats))
scomber_G1_med <- scomber_G1 %>% group_by(harbour) %>% summarise(median(scomber_catches), median(scomber_fishingdays), median(scomber_boats))
trachurus_G1_med <- trachurus_G1 %>% group_by(harbour) %>% summarise(median(trachurus_catches), median(trachurus_fishingdays), median(trachurus_boats))

# Median of variables fish zone GSA-06
data_G6_med <- data_G6 %>% group_by(harbour) %>% summarise(median(bogue_catches), median(bogue_fishingdays), median(bogue_boats), median(anchovy_catches), median(anchovy_fishingdays), median(anchovy_boats), median(sardina_catches), median(sardina_fishingdays), median(sardina_boats), median(sardinella_catches), median(sardinella_fishingdays), median(sardinella_boats), median(scomber_catches), median(scomber_fishingdays), median(scomber_boats), median(trachurus_catches), median(trachurus_fishingdays), median(trachurus_boats))
anchovy_G6_med <- anchovy_G6 %>% group_by(harbour) %>% summarise(median(anchovy_catches), median(anchovy_fishingdays), median(anchovy_boats))
bogue_G6_med <- bogue_G6 %>% group_by(harbour) %>% summarise(median(bogue_catches), median(bogue_fishingdays), median(bogue_boats))
sardine_G6_med <- sardine_G6 %>% group_by(harbour) %>% summarise(median(sardina_catches), median(sardina_fishingdays), median(sardina_boats))
sardinella_G6_med <- sardinella_G6 %>% group_by(harbour) %>% summarise(median(sardinella_catches), median(sardinella_fishingdays), median(sardinella_boats))
scomber_G6_med <- scomber_G6 %>% group_by(harbour) %>% summarise(median(scomber_catches), median(scomber_fishingdays), median(scomber_boats))
trachurus_G6_med <- trachurus_G6 %>% group_by(harbour) %>% summarise(median(trachurus_catches), median(trachurus_fishingdays), median(trachurus_boats))

# Data standardization fish zone GSA-01
data_G1_scale <- scale(data_G1_med[,2:19])
anchovy_G1_scale <- scale(anchovy_G1_med[, 2:4])
bogue_G1_scale <- scale(bogue_G1_med[, 2:4])
sardine_G1_scale <- scale(sardine_G1_med[, 2:4])
sardinella_G1_scale <- scale(sardinella_G1_med[, 2:4])
scomber_G1_scale <- scale(scomber_G1_med[, 2:4])
trachurus_G1_scale <- scale(trachurus_G1_med[, 2:4])

rownames(data_G1_scale) <- data_G1_med$harbour
row.names(anchovy_G1_scale) <- anchovy_G1_med$harbour
row.names(bogue_G1_scale) <- bogue_G1_med$harbour
row.names(sardine_G1_scale) <- sardine_G1_med$harbour
row.names(sardinella_G1_scale) <- sardinella_G1_med$harbour
row.names(scomber_G1_scale) <- scomber_G1_med$harbour
row.names(trachurus_G1_scale) <- trachurus_G1_med$harbour

# Data standardization fish zone GSA-06
data_G6_scale <- scale(data_G6_med[,2:19])
anchovy_G6_scale <- scale(anchovy_G6_med[, 2:4])
bogue_G6_scale <- scale(bogue_G6_med[, 2:4])
sardine_G6_scale <- scale(sardine_G6_med[, 2:4])
sardinella_G6_scale <- scale(sardinella_G6_med[, 2:4])
scomber_G6_scale <- scale(scomber_G6_med[, 2:4])
trachurus_G6_scale <- scale(trachurus_G6_med[, 2:4])

rownames(data_G6_scale) <- data_G6_med$harbour
row.names(anchovy_G6_scale) <- anchovy_G6_med$harbour
row.names(bogue_G6_scale) <- bogue_G6_med$harbour
row.names(sardine_G6_scale) <- sardine_G6_med$harbour
row.names(sardinella_G6_scale) <- sardinella_G6_med$harbour
row.names(scomber_G6_scale) <- scomber_G6_med$harbour
row.names(trachurus_G6_scale) <- trachurus_G6_med$harbour

res.pca <- PCA(data_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete Dataset Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 16. Cluster allocation: Complete Dataset Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : Complete Dataset Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: Complete Dataset Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=8, title = "Top 4 most influential variables in the PCA Cluster: Complete Dataset Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(data_G1_scale, centers = 2, nstart = 15)
k3 <- kmeans(data_G1_scale, centers = 3, nstart = 15)
k4 <- kmeans(data_G1_scale, centers = 4, nstart = 15)
k5 <- kmeans(data_G1_scale, centers = 5, nstart = 15)
k6 <- kmeans(data_G1_scale, centers = 6, nstart = 15)
k7 <- kmeans(data_G1_scale, centers = 7, nstart = 15)
k8 <- kmeans(data_G1_scale, centers = 8, nstart = 15)
k9 <- kmeans(data_G1_scale, centers = 9, nstart = 15)

p1 <- fviz_cluster(k2, geom = "point", data = data_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = data_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = data_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = data_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = data_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = data_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = data_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = data_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_data <- hkmeans(data_G1_scale, 6)
fviz_dend(res.hk_data, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : Complete Dataset Fish Zone PS-SPF-G1")
fviz_cluster(res.hk_data, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: Complete Dataset Fish Zone PS-SPF-G1")
res.pca <- PCA(anchovy_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete Anchovy - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 17.Cluster allocation: Anchovy - Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : Anchovy - Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: Anchovy - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Anhovy - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(anchovy_G1_scale, centers = 2, nstart = 16)
k3 <- kmeans(anchovy_G1_scale, centers = 3, nstart = 16)
k4 <- kmeans(anchovy_G1_scale, centers = 4, nstart = 16)
k5 <- kmeans(anchovy_G1_scale, centers = 5, nstart = 16)
k6 <- kmeans(anchovy_G1_scale, centers = 6, nstart = 16)
k7 <- kmeans(anchovy_G1_scale, centers = 7, nstart = 16)
k8 <- kmeans(anchovy_G1_scale, centers = 8, nstart = 16)
k9 <- kmeans(anchovy_G1_scale, centers = 9, nstart = 16)

p1 <- fviz_cluster(k2, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = anchovy_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_anchovy <- hkmeans(anchovy_G1_scale, 6)
fviz_dend(res.hk_anchovy, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : Anchovy - Alboran Sea GSA-01")
fviz_cluster(res.hk_anchovy, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: Anchovy - Alboran Sea GSA-01")
res.pca <- PCA(bogue_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete bogue - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 18. Cluster allocation: bogue - Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : bogue - Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: bogue - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Bogue - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(bogue_G1_scale, centers = 2, nstart = 15)
k3 <- kmeans(bogue_G1_scale, centers = 3, nstart = 15)
k4 <- kmeans(bogue_G1_scale, centers = 4, nstart = 15)
k5 <- kmeans(bogue_G1_scale, centers = 5, nstart = 15)
k6 <- kmeans(bogue_G1_scale, centers = 6, nstart = 15)
k7 <- kmeans(bogue_G1_scale, centers = 7, nstart = 15)
k8 <- kmeans(bogue_G1_scale, centers = 8, nstart = 15)
k9 <- kmeans(bogue_G1_scale, centers = 9, nstart = 15)

p1 <- fviz_cluster(k2, geom = "point", data = bogue_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = bogue_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = bogue_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = bogue_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = bogue_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = bogue_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = bogue_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = bogue_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_bogue <- hkmeans(bogue_G1_scale, 6)
fviz_dend(res.hk_bogue, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : bogue - Alboran Sea GSA-01")
fviz_cluster(res.hk_bogue, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: bogue - Alboran Sea GSA-01")
res.pca <- PCA(sardine_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete sardine - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 19. Cluster allocation: sardine - Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : sardine - Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: sardine - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Sardine - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(sardine_G1_scale, centers = 2, nstart = 16)
k3 <- kmeans(sardine_G1_scale, centers = 3, nstart = 16)
k4 <- kmeans(sardine_G1_scale, centers = 4, nstart = 16)
k5 <- kmeans(sardine_G1_scale, centers = 5, nstart = 16)
k6 <- kmeans(sardine_G1_scale, centers = 6, nstart = 16)
k7 <- kmeans(sardine_G1_scale, centers = 7, nstart = 16)
k8 <- kmeans(sardine_G1_scale, centers = 8, nstart = 16)
k9 <- kmeans(sardine_G1_scale, centers = 9, nstart = 16)

p1 <- fviz_cluster(k2, geom = "point", data = sardine_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = sardine_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = sardine_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = sardine_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = sardine_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = sardine_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = sardine_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = sardine_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_sardine <- hkmeans(sardine_G1_scale, 6)
fviz_dend(res.hk_sardine, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : sardine - Alboran Sea GSA-01")
fviz_cluster(res.hk_sardine, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: sardine - Alboran Sea GSA-01")
res.pca <- PCA(sardinella_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete sardinella - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 20. Cluster allocation: sardinella - Fish Zone PS-SPF-G1") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : sardinella - Fish Zone PS-SPF-G1")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: sardinella - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Sardinella - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(sardinella_G1_scale, centers = 2, nstart = 15)
k3 <- kmeans(sardinella_G1_scale, centers = 3, nstart = 15)
k4 <- kmeans(sardinella_G1_scale, centers = 4, nstart = 15)
k5 <- kmeans(sardinella_G1_scale, centers = 5, nstart = 15)
k6 <- kmeans(sardinella_G1_scale, centers = 6, nstart = 15)
k7 <- kmeans(sardinella_G1_scale, centers = 7, nstart = 15)
k8 <- kmeans(sardinella_G1_scale, centers = 8, nstart = 15)
k9 <- kmeans(sardinella_G1_scale, centers = 9, nstart = 15)
p1 <- fviz_cluster(k2, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = sardinella_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_sardinella <- hkmeans(sardinella_G1_scale, 6)
fviz_dend(res.hk_sardinella, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : sardinella - Alboran Sea GSA-01")
fviz_cluster(res.hk_sardinella, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: sardinella - Alboran Sea GSA-01")
res.pca <- PCA(scomber_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete scomber - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 21. Cluster allocation: scomber - Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : scomber - Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: scomber - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Scomber - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(scomber_G1_scale, centers = 2, nstart = 16)
k3 <- kmeans(scomber_G1_scale, centers = 3, nstart = 16)
k4 <- kmeans(scomber_G1_scale, centers = 4, nstart = 16)
k5 <- kmeans(scomber_G1_scale, centers = 5, nstart = 16)
k6 <- kmeans(scomber_G1_scale, centers = 6, nstart = 16)
k7 <- kmeans(scomber_G1_scale, centers = 7, nstart = 16)
k8 <- kmeans(scomber_G1_scale, centers = 8, nstart = 16)
k9 <- kmeans(scomber_G1_scale, centers = 9, nstart = 16)

p1 <- fviz_cluster(k2, geom = "point", data = scomber_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = scomber_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = scomber_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = scomber_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = scomber_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = scomber_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = scomber_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = scomber_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_scomber <- hkmeans(scomber_G1_scale, 6)
fviz_dend(res.hk_scomber, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : scomber - Alboran Sea GSA-01")
fviz_cluster(res.hk_scomber, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: scomber - Alboran Sea GSA-01")
res.pca <- PCA(trachurus_G1_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete trachurus - Alboran Sea GSA-01") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 22. Cluster allocation: trachurus - Alboran Sea GSA-01") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : trachurus - Alboran Sea GSA-01")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: trachurus - Alboran Sea GSA-01")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Trachurus - Alboran Sea GSA-01")
set.seed(123)
k2 <- kmeans(trachurus_G1_scale, centers = 2, nstart = 16)
k3 <- kmeans(trachurus_G1_scale, centers = 3, nstart = 16)
k4 <- kmeans(trachurus_G1_scale, centers = 4, nstart = 16)
k5 <- kmeans(trachurus_G1_scale, centers = 5, nstart = 16)
k6 <- kmeans(trachurus_G1_scale, centers = 6, nstart = 16)
k7 <- kmeans(trachurus_G1_scale, centers = 7, nstart = 16)
k8 <- kmeans(trachurus_G1_scale, centers = 8, nstart = 16)
k9 <- kmeans(trachurus_G1_scale, centers = 9, nstart = 16)

p1 <- fviz_cluster(k2, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = trachurus_G1_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_trachurus <- hkmeans(trachurus_G1_scale, 6)
fviz_dend(res.hk_trachurus, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : trachurus - Alboran Sea GSA-01")
fviz_cluster(res.hk_trachurus, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: trachurus - Alboran Sea GSA-01")
res.pca <- PCA(data_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete Dataset Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 23. Cluster allocation: Complete Dataset Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : Complete Dataset Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: Complete Dataset Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=8, title = "Top 4 most influential variables in the PCA Cluster: Complete Dataset - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(data_G6_scale, centers = 2, nstart = 20)
k3 <- kmeans(data_G6_scale, centers = 3, nstart = 20)
k4 <- kmeans(data_G6_scale, centers = 4, nstart = 20)
k5 <- kmeans(data_G6_scale, centers = 5, nstart = 20)
k6 <- kmeans(data_G6_scale, centers = 6, nstart = 20)
k7 <- kmeans(data_G6_scale, centers = 7, nstart = 20)
k8 <- kmeans(data_G6_scale, centers = 8, nstart = 20)
k9 <- kmeans(data_G6_scale, centers = 9, nstart = 20)

p1 <- fviz_cluster(k2, geom = "point", data = data_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = data_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = data_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = data_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = data_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = data_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = data_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = data_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_data <- hkmeans(data_G6_scale, 6)
fviz_dend(res.hk_data, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : Complete Dataset Northern Spain GSA-06")
fviz_cluster(res.hk_data, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: Complete Dataset Northern Spain GSA-06")
res.pca <- PCA(anchovy_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete Anchovy - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 24. Cluster allocation: Anchovy - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : Anchovy - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: Anchovy - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Anhovy - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(anchovy_G6_scale, centers = 2, nstart = 27)
k3 <- kmeans(anchovy_G6_scale, centers = 3, nstart = 27)
k4 <- kmeans(anchovy_G6_scale, centers = 4, nstart = 27)
k5 <- kmeans(anchovy_G6_scale, centers = 5, nstart = 27)
k6 <- kmeans(anchovy_G6_scale, centers = 6, nstart = 27)
k7 <- kmeans(anchovy_G6_scale, centers = 7, nstart = 27)
k8 <- kmeans(anchovy_G6_scale, centers = 8, nstart = 27)
k9 <- kmeans(anchovy_G6_scale, centers = 9, nstart = 27)

p1 <- fviz_cluster(k2, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = anchovy_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_anchovy <- hkmeans(anchovy_G6_scale, 6)
fviz_dend(res.hk_anchovy, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : Anchovy - Northern Spain GSA-06")
fviz_cluster(res.hk_anchovy, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: Anchovy - Northern Spain GSA-06")
res.pca <- PCA(bogue_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete bogue - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 25. Cluster allocation: bogue - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : bogue - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: bogue - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Bogue - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(bogue_G6_scale, centers = 2, nstart = 20)
k3 <- kmeans(bogue_G6_scale, centers = 3, nstart = 20)
k4 <- kmeans(bogue_G6_scale, centers = 4, nstart = 20)
k5 <- kmeans(bogue_G6_scale, centers = 5, nstart = 20)
k6 <- kmeans(bogue_G6_scale, centers = 6, nstart = 20)
k7 <- kmeans(bogue_G6_scale, centers = 7, nstart = 20)
k8 <- kmeans(bogue_G6_scale, centers = 8, nstart = 20)
k9 <- kmeans(bogue_G6_scale, centers = 9, nstart = 20)

p1 <- fviz_cluster(k2, geom = "point", data = bogue_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = bogue_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = bogue_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = bogue_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = bogue_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = bogue_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = bogue_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = bogue_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_bogue <- hkmeans(bogue_G6_scale, 6)
fviz_dend(res.hk_bogue, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : bogue - Northern Spain GSA-06")
fviz_cluster(res.hk_bogue, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: bogue - Northern Spain GSA-06")
res.pca <- PCA(sardine_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete sardine - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 26. Cluster allocation: sardine - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : sardine - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: sardine - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Sardine - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(sardine_G6_scale, centers = 2, nstart = 25)
k3 <- kmeans(sardine_G6_scale, centers = 3, nstart = 25)
k4 <- kmeans(sardine_G6_scale, centers = 4, nstart = 25)
k5 <- kmeans(sardine_G6_scale, centers = 5, nstart = 25)
k6 <- kmeans(sardine_G6_scale, centers = 6, nstart = 25)
k7 <- kmeans(sardine_G6_scale, centers = 7, nstart = 25)
k8 <- kmeans(sardine_G6_scale, centers = 8, nstart = 25)
k9 <- kmeans(sardine_G6_scale, centers = 9, nstart = 25)

p1 <- fviz_cluster(k2, geom = "point", data = sardine_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = sardine_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = sardine_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = sardine_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = sardine_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = sardine_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = sardine_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = sardine_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_sardine <- hkmeans(sardine_G6_scale, 6)
fviz_dend(res.hk_sardine, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : sardine - Northern Spain GSA-06")
fviz_cluster(res.hk_sardine, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: sardine - Northern Spain GSA-06")
res.pca <- PCA(sardinella_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete sardinella - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 27. Cluster allocation: sardinella - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : sardinella - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: sardinella - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Sardinella - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(sardinella_G6_scale, centers = 2, nstart = 22)
k3 <- kmeans(sardinella_G6_scale, centers = 3, nstart = 22)
k4 <- kmeans(sardinella_G6_scale, centers = 4, nstart = 22)
k5 <- kmeans(sardinella_G6_scale, centers = 5, nstart = 22)
k6 <- kmeans(sardinella_G6_scale, centers = 6, nstart = 22)
k7 <- kmeans(sardinella_G6_scale, centers = 7, nstart = 22)
k8 <- kmeans(sardinella_G6_scale, centers = 8, nstart = 22)
k9 <- kmeans(sardinella_G6_scale, centers = 9, nstart = 22)

p1 <- fviz_cluster(k2, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = sardinella_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_sardinella <- hkmeans(sardinella_G6_scale, 6)
fviz_dend(res.hk_sardinella, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : sardinella - Northern Spain GSA-06")
fviz_cluster(res.hk_sardinella, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: sardinella - Northern Spain GSA-06")
res.pca <- PCA(scomber_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete scomber - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 28. Cluster allocation: scomber - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : scomber - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: scomber - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Scomber - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(scomber_G6_scale, centers = 2, nstart = 23)
k3 <- kmeans(scomber_G6_scale, centers = 3, nstart = 23)
k4 <- kmeans(scomber_G6_scale, centers = 4, nstart = 23)
k5 <- kmeans(scomber_G6_scale, centers = 5, nstart = 23)
k6 <- kmeans(scomber_G6_scale, centers = 6, nstart = 23)
k7 <- kmeans(scomber_G6_scale, centers = 7, nstart = 23)
k8 <- kmeans(scomber_G6_scale, centers = 8, nstart = 23)
k9 <- kmeans(scomber_G6_scale, centers = 9, nstart = 23)

p1 <- fviz_cluster(k2, geom = "point", data = scomber_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = scomber_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = scomber_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = scomber_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = scomber_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = scomber_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = scomber_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = scomber_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_scomber <- hkmeans(scomber_G6_scale, 6)
fviz_dend(res.hk_scomber, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : scomber - Northern Spain GSA-06")
fviz_cluster(res.hk_scomber, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: scomber - Northern Spain GSA-06")
res.pca <- PCA(trachurus_G6_scale, ncp = 3, graph = FALSE)
fviz_pca_biplot(res.pca, title = "PCA - Biplot Complete trachurus - Northern Spain GSA-06") + theme_minimal()
res.hcpc <- HCPC(res.pca, graph = FALSE)
res.hcpc$data.clust %>% kbl(caption = "Table 29. Cluster allocation: trachurus - Northern Spain GSA-06") %>% kable_classic(full_width = F, html_font = "Calibri")
fviz_dend(res.hcpc, cex = 0.7, palette = "jco", rect = TRUE, rect_fill = TRUE, rect_border = "jco", labels_track_height = 0.8, main = "PCA Cluster dendogram: : trachurus - Northern Spain GSA-06")
fviz_cluster(res.hcpc, repel = TRUE, show.clust.cent = TRUE, palette = "jco", ggtheme = theme_minimal(), main = "Factor map", title = "PCA Cluster plot: trachurus - Northern Spain GSA-06")
res.hcpc$desc.var$quanti
fviz_contrib(res.pca, choice="var", axes=1, top=4, title = "Influential level of variables in the PCA Cluster: Trachurus - Northern Spain GSA-06")
set.seed(123)
k2 <- kmeans(trachurus_G6_scale, centers = 2, nstart = 23)
k3 <- kmeans(trachurus_G6_scale, centers = 3, nstart = 23)
k4 <- kmeans(trachurus_G6_scale, centers = 4, nstart = 23)
k5 <- kmeans(trachurus_G6_scale, centers = 5, nstart = 23)
k6 <- kmeans(trachurus_G6_scale, centers = 6, nstart = 23)
k7 <- kmeans(trachurus_G6_scale, centers = 7, nstart = 23)
k8 <- kmeans(trachurus_G6_scale, centers = 8, nstart = 23)
k9 <- kmeans(trachurus_G6_scale, centers = 9, nstart = 23)

p1 <- fviz_cluster(k2, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 2")
p2 <- fviz_cluster(k3, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 3")
p3 <- fviz_cluster(k4, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 4")
p4 <- fviz_cluster(k5, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 5")
p5 <- fviz_cluster(k6, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 6")
p6 <- fviz_cluster(k7, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 7")
p7 <- fviz_cluster(k8, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 8")
p8 <- fviz_cluster(k9, geom = "point", data = trachurus_G6_scale) + ggtitle("k = 9")
grid.arrange(p1, p2, p3, p4, p5, p6, p7, p8, nrow = 4)

res.hk_trachurus <- hkmeans(trachurus_G6_scale, 6)
fviz_dend(res.hk_trachurus, cex = 0.6, palette = "jco", rect = TRUE, rect_border = "jco", rect_fill = TRUE, main = "K-means Cluster dendogram: : trachurus - Northern Spain GSA-06")
fviz_cluster(res.hk_trachurus, palette = "jco", repel = TRUE, ggtheme = theme_classic(), title = "K-means Cluster plot: trachurus - Northern Spain GSA-06")